text:
,
pages = {499–538},
numpages = {40},
keywords = {Part-of-speech tagging, Narrative medical reports, Text classification, Artificial intelligence, Negation}
}

@article{10.1007/s10791-008-9060-1,
author = {Wang, Jun and Robertson, Stephen and Vries, Arjen P. and Reinders, Marcel J.},
title = {Probabilistic Relevance Ranking for Collaborative Filtering},
year = {2008},
issue_date = {December  2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9060-1},
doi = {10.1007/s10791-008-9060-1},
abstract = {Collaborative filtering is concerned with making recommendations about items to users. Most formulations of the problem are specifically designed for predicting user ratings, assuming past data of explicit user ratings is available. However, in practice we may only have implicit evidence of user preference; and furthermore, a better view of the task is of generating a top-N list of items that the user is most likely to like. In this regard, we argue that collaborative filtering can be directly cast as a  relevance  ranking problem. We begin with the classic Probability Ranking Principle of information retrieval, proposing a probabilistic item ranking framework. In the framework, we derive two different ranking models, showing that despite their common origin, different factorizations reflect two distinctive ways to approach item ranking. For the model estimations, we limit our discussions to implicit user preference data, and adopt an approximation method introduced in the classic text retrieval model (i.e. the Okapi BM25 formula) to effectively decouple frequency counts and presence/absence counts in the preference data. Furthermore, we extend the basic formula by proposing the Bayesian inference to estimate the probability of relevance (and non-relevance), which largely alleviates the data sparsity problem. Apart from a theoretical contribution, our experiments on real data sets demonstrate that the proposed methods perform significantly better than other strong baselines.},
journal = {Inf. Retr.},
month = dec,
pages = {477–497},
numpages = {21},
keywords = {Relevance ranking, Personalization, Probability Ranking Principle, Collaborative filtering, Recommender systems}
}

@article{10.1007/s10791-008-9064-x,
author = {Murdock, Vanessa},
title = {Ellen Voorhees and Donna Harman (Eds): TREC Experiment and Evaluation in Information Retrieval},
year = {2008},
issue_date = {October   2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9064-x},
doi = {10.1007/s10791-008-9064-x},
journal = {Inf. Retr.},
month = oct,
pages = {473–475},
numpages = {3}
}

@article{10.1007/s10791-008-9063-y,
author = {White, Bebo},
title = {Amy Langville and Carl Meyer, Google's Page Rank and Beyond: The Science of Search Engine Rankings},
year = {2008},
issue_date = {October   2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9063-y},
doi = {10.1007/s10791-008-9063-y},
journal = {Inf. Retr.},
month = oct,
pages = {471–472},
numpages = {2}
}

@article{10.1007/s10791-008-9059-7,
author = {Sakai, Tetsuya and Kando, Noriko},
title = {On Information Retrieval Metrics Designed for Evaluation with Incomplete Relevance Assessments},
year = {2008},
issue_date = {October   2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9059-7},
doi = {10.1007/s10791-008-9059-7},
abstract = {Modern information retrieval (IR) test collections have grown in size, but the available manpower for relevance assessments has more or less remained constant. Hence, how to reliably evaluate and compare IR systems using  incomplete  relevance data, where many documents exist that were never examined by the relevance assessors, is receiving a lot of attention. This article compares the robustness of IR metrics to incomplete relevance assessments, using four different sets of graded-relevance test collections with submitted runs--the TREC 2003 and 2004 robust track data and the NTCIR-6 Japanese and Chinese IR data from the crosslingual task. Following previous work, we artificially reduce the original relevance data to simulate IR evaluation environments with extremely incomplete relevance data. We then investigate the effect of this reduction on  discriminative power , which we define as the proportion of system pairs with a statistically significant difference for a given probability of Type I Error, and on  Kendall's rank correlation , which reflects the overall resemblance of two system rankings according to two different metrics or two different relevance data sets. According to these experiments, Q , nDCG and AP proposed by Sakai are superior to bpref proposed by Buckley and Voorhees and to Rank-Biased Precision proposed by Moffat and Zobel. We also point out some weaknesses of bpref and Rank-Biased Precision by examining their formal definitions.},
journal = {Inf. Retr.},
month = oct,
pages = {447–470},
numpages = {24},
keywords = {Evaluation metrics, Incompleteness, Relevance assessments, Test collections}
}

@article{10.1007/s10791-008-9058-8,
author = {Talvensaari, Tuomas and Pirkola, Ari and J\"{a}rvelin, Kalervo and Juhola, Martti and Laurikkala, Jorma},
title = {Focused Web Crawling in the Acquisition of Comparable Corpora},
year = {2008},
issue_date = {October   2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9058-8},
doi = {10.1007/s10791-008-9058-8},
abstract = {Cross-Language Information Retrieval (CLIR) resources, such as dictionaries and parallel corpora, are scarce for special domains. Obtaining comparable corpora automatically for such domains could be an answer to this problem. The Web, with its vast volumes of data, offers a natural source for this. We experimented with focused crawling as a means to acquire comparable corpora in the genomics domain. The acquired corpora were used to statistically translate domain-specific words. The same words were also translated using a high-quality, but non-genomics-related parallel corpus, which fared considerably worse. We also evaluated our system with standard information retrieval (IR) experiments, combining statistical translation using the Web corpora with dictionary-based translation. The results showed improvement over pure dictionary-based translation. Therefore, mining the Web for comparable corpora seems promising.},
journal = {Inf. Retr.},
month = oct,
pages = {427–445},
numpages = {19},
keywords = {Focused crawling, Cross-language information retrieval, Comparable corpora}
}

@article{10.1007/s10791-008-9055-y,
author = {Magdy, Walid and Darwish, Kareem},
title = {Effect of OCR Error Correction on Arabic Retrieval},
year = {2008},
issue_date = {October   2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9055-y},
doi = {10.1007/s10791-008-9055-y},
abstract = {Arabic documents that are available only in print continue to be ubiquitous and they can be scanned and subsequently OCR'ed to ease their retrieval. This paper explores the effect of context-based OCR correction on the effectiveness of retrieving Arabic OCR documents using different index terms. Different OCR correction techniques based on language modeling with different correction abilities were tested on real OCR and synthetic OCR degradation. Results show that the reduction of word error rates needs to pass a certain limit to get a noticeable effect on retrieval. If only moderate error reduction is available, then using short character n-gram for retrieval without error correction is not a bad strategy. Word-based correction in conjunction with language modeling had a statistically significant impact on retrieval even for character 3-grams, which are known to be among the best index terms for OCR degraded Arabic text. Further, using a sufficiently large language model for correction can minimize the need for morphologically sensitive error correction.},
journal = {Inf. Retr.},
month = oct,
pages = {405–425},
numpages = {21},
keywords = {OCR, Error correction, Information retrieval, Language modeling}
}

@article{10.1007/s10791-008-9053-0,
author = {Si, Luo and Yu, Danni and Kihara, Daisuke and Fang, Yi},
title = {Combining Gene Sequence Similarity and Textual Information for Gene Function Annotation in the Literature},
year = {2008},
issue_date = {October   2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9053-0},
doi = {10.1007/s10791-008-9053-0},
abstract = {Annotation of the functions of genes and proteins is an essential step in genome analysis. Information extraction techniques have been proposed to obtain the function information of genes and proteins in the biomedical literature. However, the performance of most information extraction techniques of function annotation in the biomedical literature is not satisfactory due to the large variability in the expression of concepts in the biomedical literature. This paper proposes a framework to improve the gene function annotation in the literature by considering both the textual information in the literature and the functions of genes with sequences similar to a target gene. The new framework collects multiple types of evidence as: (i) textual information about gene functions by matching keywords of the gene functions; (ii) gene function information from the known functions of genes with sequences similar to a target gene; and (iii) the prior probabilities of gene functions to be associated with an arbitrary gene by mining the known gene functions from curated databases. A supervised learning method is utilized to obtain the weights for combining the three types of evidence to assign appropriate Gene Ontology terms for target genes. Empirical studies on two testbeds demonstrate that the combination of sequence similarity scores, function prior probabilities and textual information improves the accuracy of gene function annotation in the literature. The  F -measure scores obtained with the proposed framework are substantially higher than the scores of the solutions in prior research.},
journal = {Inf. Retr.},
month = oct,
pages = {389–404},
numpages = {16},
keywords = {Biomedical literature mining, Combination of multiple evidence, Gene function annotation}
}

@article{10.1007/s10791-008-9054-z,
author = {Fredriksson, Kimmo and Grabowski, Szymon},
title = {Efficient Algorithms for Pattern Matching with General Gaps, Character Classes, and Transposition Invariance},
year = {2008},
issue_date = {August    2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9054-z},
doi = {10.1007/s10791-008-9054-z},
abstract = {We develop efficient dynamic programming algorithms for pattern matching with general gaps and character classes. We consider patterns of the form  p   0   g (  a   0 ,  b   0 )  p   1   g (  a   1 ,  b   1 )  p    m  1 , where  p    i   Σ, Σ is some finite alphabet, and  g (  a    i  ,  b    i  ) denotes a gap of length  a    i    b    i   between symbols  p    i   and  p    i +1 . The text symbol  t    j   matches  p    i   iff  t    j    p    i  . Moreover, we require that if  p    i   matches  t    j  , then  p    i +1  should match one of the text symbols  $$ t_{j+a_i+1} ldots t_{j+b_i+1}.$$  Either or both of  a    i   and  b    i   can be negative. We also consider transposition invariant matching, i.e., the matching condition becomes  t    j    p    i   +  , for some constant   determined by the algorithms. We give algorithms that have efficient average and worst case running times. The algorithms have important applications in music information retrieval and computational biology. We give experimental results showing that the algorithms work well in practice.},
journal = {Inf. Retr.},
month = aug,
pages = {335–357},
numpages = {23},
keywords = {Transposition invariance, Bounded length gaps, Character classes, Sparse dynamic programming, String matching}
}

@article{10.1007/s10791-008-9052-1,
author = {Crestani, Fabio and Ferragina, Paolo and Sanderson, Mark},
title = {Preface},
year = {2008},
issue_date = {August    2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9052-1},
doi = {10.1007/s10791-008-9052-1},
journal = {Inf. Retr.},
month = aug,
pages = {267–268},
numpages = {2}
}

@article{10.1007/s10791-008-9050-3,
author = {Russo, Lu\'{\i}s M. and Oliveira, Arlindo L.},
title = {A Compressed Self-Index Using a Ziv---Lempel Dictionary},
year = {2008},
issue_date = {August    2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9050-3},
doi = {10.1007/s10791-008-9050-3},
abstract = {A compressed full-text self-index for a text  T , of size  u , is a data structure used to search for patterns  P , of size  m , in  T , that requires reduced space, i.e. space that depends on the empirical entropy (  H    k   or  H   0 ) of  T , and is, furthermore, able to reproduce any substring of  T . In this paper we present a new compressed self-index able to locate the occurrences of  P  in  O ((  m  +  occ )log  u ) time, where  occ  is the number of occurrences. The fundamental improvement over previous LZ78 based indexes is the reduction of the search time dependency on  m  from  O (  m   2 ) to  O (  m ). To achieve this result we point out the main obstacle to linear time algorithms based on LZ78 data compression and expose and explore the nature of a recurrent structure in LZ-indexes, the  $${mathcal{T}}_{78}$$  suffix tree. We show that our method is very competitive in practice by comparing it against other state of the art compressed indexes.},
journal = {Inf. Retr.},
month = aug,
pages = {359–388},
numpages = {30},
keywords = {Compressed index, Pattern matching, Text indexing, Data compression}
}

@article{10.1007/s10791-008-9048-x,
author = {Bast, Holger and Mortensen, Christian W. and Weber, Ingmar},
title = {Output-Sensitive Autocompletion Search},
year = {2008},
issue_date = {August    2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9048-x},
doi = {10.1007/s10791-008-9048-x},
abstract = {We consider the following autocompletion search scenario: imagine a user of a search engine typing a query; then with every keystroke display those completions of the last query word that would lead to the best hits, and also display the best such hits. The following problem is at the core of this feature: for a fixed document collection, given a set  D  of documents, and an alphabetical range  W  of words, compute the set of all word-in-document pairs (  w ,  d ) from the collection such that  w   W  and  d   D . We present a new data structure with the help of which such autocompletion queries can be processed, on the average, in time linear in the input plus output size, independent of the size of the underlying document collection. At the same time, our data structure uses no more space than an inverted index. Actual query processing times on a large test collection correlate almost perfectly with our theoretical bound.},
journal = {Inf. Retr.},
month = aug,
pages = {269–286},
numpages = {18},
keywords = {Output-sensitive, Index data structure, Autocompletion, Prefix search}
}

@article{10.1007/s10791-008-9047-y,
author = {Esuli, Andrea and Fagni, Tiziano and Sebastiani, Fabrizio},
title = {Boosting Multi-Label Hierarchical Text Categorization},
year = {2008},
issue_date = {August    2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9047-y},
doi = {10.1007/s10791-008-9047-y},
abstract = {  Hierarchical Text Categorization  (HTC) is the task of generating (usually by means of supervised learning algorithms) text classifiers that operate on hierarchically structured classification schemes. Notwithstanding the fact that most large-sized classification schemes for text have a hierarchical structure, so far the attention of text classification researchers has mostly focused on algorithms for "flat" classification, i.e. algorithms that operate on non-hierarchical classification schemes. These algorithms, once applied to a hierarchical classification problem, are not capable of taking advantage of the information inherent in the class hierarchy, and may thus be suboptimal, in terms of efficiency and/or effectiveness. In this paper we propose  TreeBoost.MH , a multi-label HTC algorithm consisting of a hierarchical variant of  AdaBoost.MH , a very well-known member of the family of "boosting" learning algorithms.  TreeBoost.MH  embodies several intuitions that had arisen before within HTC: e.g. the intuitions that both feature selection and the selection of negative training examples should be performed "locally", i.e. by paying attention to the topology of the classification scheme. It also embodies the novel intuition that the weight distribution that boosting algorithms update at every boosting round should likewise be updated "locally". All these intuitions are embodied within  TreeBoost.MH  in an elegant and simple way, i.e. by defining  TreeBoost.MH  as a recursive algorithm that uses  AdaBoost.MH  as its base step, and that recurs over the tree structure. We present the results of experimenting  TreeBoost.MH  on three HTC benchmarks, and discuss analytically its computational cost.},
journal = {Inf. Retr.},
month = aug,
pages = {287–313},
numpages = {27},
keywords = {Hierarchical text classification, Boosting}
}

@article{10.1007/s10791-008-9046-z,
author = {Farah, Mohamed and Vanderpooten, Daniel},
title = {An Outranking Approach for Information Retrieval},
year = {2008},
issue_date = {August    2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9046-z},
doi = {10.1007/s10791-008-9046-z},
abstract = {Over the last three decades, research in Information Retrieval (IR) shows performance improvement when many sources of evidence are combined to produce a ranking of documents. Most current approaches assess document relevance by computing a single score which aggregates values of some attributes or criteria. They use analytic aggregation operators which either lead to a loss of valuable information, e.g., the min or lexicographic operators, or allow very bad scores on some criteria to be compensated with good ones, e.g., the weighted sum operator. Moreover, all these approaches do not handle imprecision of criterion scores. In this paper, we propose a multiple criteria framework using a new aggregation mechanism based on decision rules identifying positive and negative reasons for judging whether a document should get a better ranking than another. The resulting procedure also handles imprecision in criteria design. Experimental results are reported showing that the suggested method performs better than standard aggregation operators.},
journal = {Inf. Retr.},
month = aug,
pages = {315–334},
numpages = {20},
keywords = {Information retrieval, Outranking approach, Aggregation, Multiple criteria, Relevance}
}

@article{10.1007/s10791-008-9045-0,
author = {Fuhr, Norbert},
title = {A Probability Ranking Principle for Interactive Information Retrieval},
year = {2008},
issue_date = {June      2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9045-0},
doi = {10.1007/s10791-008-9045-0},
abstract = {The classical Probability Ranking Principle (PRP) forms the theoretical basis for probabilistic Information Retrieval (IR) models, which are dominating IR theory since about 20 years. However, the assumptions underlying the PRP often do not hold, and its view is too narrow for interactive information retrieval (IIR). In this article, a new theoretical framework for interactive retrieval is proposed: The basic idea is that during IIR, a user moves between situations. In each situation, the system presents to the user a list of choices, about which s/he has to decide, and the first positive decision moves the user to a new situation. Each choice is associated with a number of cost and probability parameters. Based on these parameters, an optimum ordering of the choices can the derived--the PRP for IIR. The relationship of this rule to the classical PRP is described, and issues of further research are pointed out.},
journal = {Inf. Retr.},
month = jun,
pages = {251–265},
numpages = {15},
keywords = {Interactive retrieval, Optimum retrieval rule, Probabilistic retrieval}
}

@article{10.1007/s10791-008-9044-1,
author = {Wu, Yi-Fang Brook and Li, Quanzhi},
title = {Document Keyphrases as Subject Metadata: Incorporating Document Key Concepts in Search Results},
year = {2008},
issue_date = {June      2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9044-1},
doi = {10.1007/s10791-008-9044-1},
abstract = {Most search engines display some document metadata, such as title, snippet and URL, in conjunction with the returned hits to aid users in determining documents. However, metadata is usually fragmented pieces of information that, even when combined, does not provide an overview of a returned document. In this paper, we propose a mechanism of enriching metadata of the returned results by incorporating automatically extracted document keyphrases with each returned hit. We hypothesize that keyphrases of a document can better represent the major theme in that document. Therefore, by examining the keyphrases in each returned hit, users can better predict the content of documents and the time spent on downloading and examining the irrelevant documents will be reduced substantially.},
journal = {Inf. Retr.},
month = jun,
pages = {229–249},
numpages = {21},
keywords = {Document keyphrase, Keyphrase extraction, Search interface, Document metadata, Document surrogate}
}

@article{10.1007/s10791-007-9043-7,
author = {Keskustalo, Heikki and J\"{a}rvelin, Kalervo and Pirkola, Ari},
title = {Evaluating the Effectiveness of Relevance Feedback Based on a User Simulation Model: Effects of a User Scenario on Cumulated Gain Value},
year = {2008},
issue_date = {June      2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9043-7},
doi = {10.1007/s10791-007-9043-7},
abstract = {We propose a method for performing evaluation of relevance feedback based on simulating real users. The user simulation applies a model defining the user's relevance threshold to accept individual documents as feedback in a graded relevance environment; user's patience to browse the initial list of retrieved documents; and his/her effort in providing the feedback. We evaluate the result by using cumulated gain-based evaluation together with freezing all documents seen by the user in order to simulate the point of view of a user who is browsing the documents during the retrieval process. We demonstrate the method by performing a simulation in the laboratory setting and present the "branching" curve sets characteristic for the presented evaluation method. Both the average and topic-by-topic results indicate that if the freezing approach is adopted, giving feedback of mixed quality makes sense for various usage scenarios even though the modeled users prefer finding especially the most relevant documents.},
journal = {Inf. Retr.},
month = jun,
pages = {209–228},
numpages = {20},
keywords = {Simulation, Relevance feedback, Evaluation, User modeling}
}

@article{10.1007/s10791-007-9042-8,
author = {B\"{u}ttcher, Stefan and Clarke, Charles L.},
title = {Hybrid Index Maintenance for Contiguous Inverted Lists},
year = {2008},
issue_date = {June      2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9042-8},
doi = {10.1007/s10791-007-9042-8},
abstract = {Index maintenance strategies employed by dynamic text retrieval systems based on inverted files can be divided into two categories: merge-based and in-place update strategies. Within each category, individual update policies can be distinguished based on whether they store their on-disk posting lists in a contiguous or in a discontiguous fashion. Contiguous inverted lists, in general, lead to higher query performance, by minimizing the disk seek overhead at query time, while discontiguous inverted lists lead to higher update performance, requiring less effort during index maintenance operations. In this paper, we focus on retrieval systems with high query load, where the on-disk posting lists have to be stored in a contiguous fashion at all times. We discuss a combination of re-merge and in-place index update, called H  ybrid  I  mmediate  M  erge . The method performs strictly better than the re-merge baseline policy used in our experiments, as it leads to the same query performance, but substantially better update performance. The actual time savings achievable depend on the size of the text collection being indexed; a larger collection results in greater savings. In our experiments, variations of H  ybrid  I  mmediate  M  erge  were able to reduce the total index update overhead by up to 73% compared to the re-merge baseline.},
journal = {Inf. Retr.},
month = jun,
pages = {175–207},
numpages = {33},
keywords = {Text retrieval, Search engines, Index maintenance}
}

@article{10.1007/s10791-008-9057-9,
author = {Chevalier, Max},
title = {Zdravko Markov and Daniel T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage},
year = {2008},
issue_date = {April     2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9057-9},
doi = {10.1007/s10791-008-9057-9},
journal = {Inf. Retr.},
month = apr,
pages = {169–174},
numpages = {6}
}

@article{10.1007/s10791-008-9056-x,
author = {Taksa, Isak},
title = {David Taniar: Research and Trends in Data Mining Technologies and Applications},
year = {2008},
issue_date = {April     2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9056-x},
doi = {10.1007/s10791-008-9056-x},
journal = {Inf. Retr.},
month = apr,
pages = {165–167},
numpages = {3}
}

@article{10.1007/s10791-007-9041-9,
author = {Shakery, Azadeh and Zhai, Chengxiang},
title = {Smoothing Document Language Models with Probabilistic Term Count Propagation},
year = {2008},
issue_date = {April     2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9041-9},
doi = {10.1007/s10791-007-9041-9},
abstract = {Smoothing of document language models is critical in language modeling approaches to information retrieval. In this paper, we present a novel way of smoothing document language models based on propagating term counts probabilistically in a graph of documents. A key difference between our approach and previous approaches is that our smoothing algorithm can iteratively propagate counts and achieve smoothing with  remotely  related documents. Evaluation results on several TREC data sets show that the proposed method significantly outperforms the simple collection-based smoothing method. Compared with those other smoothing methods that also exploit local corpus structures, our method is especially effective in improving precision in top-ranked documents through "filling in" missing query terms in relevant documents, which is attractive since most users only pay attention to the top-ranked documents in search engine applications.},
journal = {Inf. Retr.},
month = apr,
pages = {139–164},
numpages = {26},
keywords = {Probabilistic propagation, Term count propagation, Language models, Smoothing}
}

@article{10.1007/s10791-007-9040-x,
author = {Losada, David E. and Azzopardi, Leif},
title = {An Analysis on Document Length Retrieval Trends in Language Modeling Smoothing},
year = {2008},
issue_date = {April     2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9040-x},
doi = {10.1007/s10791-007-9040-x},
abstract = {Document length is widely recognized as an important factor for adjusting retrieval systems. Many models tend to favor the retrieval of either short or long documents and, thus, a length-based correction needs to be applied for avoiding any length bias. In Language Modeling for Information Retrieval, smoothing methods are applied to move probability mass from document terms to unseen words, which is often dependant upon document length. In this article, we perform an in-depth study of this behavior, characterized by the document length retrieval trends, of three popular smoothing methods across a number of factors, and its impact on the length of documents retrieved and retrieval performance. First, we theoretically analyze the Jelinek---Mercer, Dirichlet prior and two-stage smoothing strategies and, then, conduct an empirical analysis. In our analysis we show how Dirichlet prior smoothing caters for document length more appropriately than Jelinek---Mercer smoothing which leads to its superior retrieval performance. In a follow up analysis, we posit that length-based priors can be used to offset any bias in the length retrieval trends stemming from the retrieval formula derived by the smoothing technique. We show that the performance of Jelinek---Mercer smoothing can be significantly improved by using such a prior, which provides a natural and simple alternative to decouple the query and document modeling roles of smoothing. With the analysis of retrieval behavior conducted in this article, it is possible to understand why the Dirichlet Prior smoothing performs better than the Jelinek---Mercer, and why the performance of the Jelinek---Mercer method is improved by including a length-based prior.},
journal = {Inf. Retr.},
month = apr,
pages = {109–138},
numpages = {30},
keywords = {Document length, Smoothing, Language models}
}

@article{10.1007/s10791-007-9039-3,
author = {Deselaers, Thomas and Keysers, Daniel and Ney, Hermann},
title = {Features for Image Retrieval: An Experimental Comparison},
year = {2008},
issue_date = {April     2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9039-3},
doi = {10.1007/s10791-007-9039-3},
abstract = {An experimental comparison of a large number of different image descriptors for content-based image retrieval is presented. Many of the papers describing new techniques and descriptors for content-based image retrieval describe their newly proposed methods as most appropriate without giving an in-depth comparison with all methods that were proposed earlier. In this paper, we first give an overview of a large variety of features for content-based image retrieval and compare them quantitatively on four different tasks: stock photo retrieval, personal photo collection retrieval, building retrieval, and medical image retrieval. For the experiments, five different, publicly available image databases are used and the retrieval performance of the features is analyzed in detail. This allows for a direct comparison of all features considered in this work and furthermore will allow a comparison of newly proposed features to these in the future. Additionally, the correlation of the features is analyzed, which opens the way for a simple and intuitive method to find an initial set of suitable features for a new task. The article concludes with recommendations which features perform well for what type of data. Interestingly, the often used, but very simple, color histogram performs well in the comparison and thus can be recommended as a simple baseline for many applications.},
journal = {Inf. Retr.},
month = apr,
pages = {77–107},
numpages = {31},
keywords = {Image classification, Quantitative comparison, Features, Image retrieval}
}

@article{10.1007/s10791-007-9038-4,
author = {Symeonidis, Panagiotis and Nanopoulos, Alexandros and Papadopoulos, Apostolos N. and Manolopoulos, Yannis},
title = {Nearest-Biclusters Collaborative Filtering Based on Constant and Coherent Values},
year = {2008},
issue_date = {February  2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9038-4},
doi = {10.1007/s10791-007-9038-4},
abstract = {Collaborative Filtering (CF) Systems have been studied extensively for more than a decade to confront the "information overload" problem. Nearest-neighbor CF is based either on similarities between users or between items, to form a neighborhood of users or items, respectively. Recent research has tried to combine the two aforementioned approaches to improve effectiveness. Traditional clustering approaches (  k -means or hierarchical clustering) has been also used to speed up the recommendation process. In this paper, we use biclustering to disclose this duality between users and items, by grouping them in both dimensions simultaneously. We propose a novel nearest-biclusters algorithm, which uses a new similarity measure that achieves partial matching of users' preferences. We apply nearest-biclusters in combination with two different types of biclustering algorithms--Bimax and xMotif--for constant and coherent biclustering, respectively. Extensive performance evaluation results in three real-life data sets are provided, which show that the proposed method improves substantially the performance of the CF process.},
journal = {Inf. Retr.},
month = feb,
pages = {51–75},
numpages = {25},
keywords = {Collaborative filtering, Clustering, Nearest neighbor, Biclustering}
}

@article{10.1007/s10791-007-9037-5,
author = {Wan, Xiaojun},
title = {Using Only Cross-Document Relationships for Both Generic and Topic-Focused Multi-Document Summarizations},
year = {2008},
issue_date = {February  2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9037-5},
doi = {10.1007/s10791-007-9037-5},
abstract = {In recent years graph-ranking based algorithms have been proposed for single document summarization and generic multi-document summarization. The algorithms make use of the "votings" or "recommendations" between sentences to evaluate the importance of the sentences in the documents. This study aims to differentiate the cross-document and within-document relationships between sentences for generic multi-document summarization and adapt the graph-ranking based algorithm for topic-focused summarization. The contributions of this study are two-fold: (1) For generic multi-document summarization, we apply the graph-based ranking algorithm based on each kind of sentence relationship and explore their relative importance for summarization performance. (2) For topic-focused multi-document summarization, we propose to integrate the relevance of the sentences to the specified topic into the graph-ranking based method. Each individual kind of sentence relationship is also differentiated and investigated in the algorithm. Experimental results on DUC 2002---DUC 2005 data demonstrate the great importance of the cross-document relationships between sentences for both generic and topic-focused multi-document summarizations. Even the approach based only on the cross-document relationships can perform better than or at least as well as the approaches based on both kinds of relationships between sentences.},
journal = {Inf. Retr.},
month = feb,
pages = {25–49},
numpages = {25},
keywords = {Multi-document summarization, Cross-document relationship, Graph-ranking, Topic-focused summarization}
}

@article{10.1007/s10791-007-9036-6,
author = {Si, Luo and Callan, Jamie and Cetintas, Suleyman and Yuan, Hao},
title = {An Effective and Efficient Results Merging Strategy for Multilingual Information Retrieval in Federated Search Environments},
year = {2008},
issue_date = {February  2008},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {11},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9036-6},
doi = {10.1007/s10791-007-9036-6},
abstract = {Multilingual information retrieval is generally understood to mean the retrieval of relevant information in multiple target languages in response to a user query in a single source language. In a multilingual federated search environment, different information sources contain documents in different languages. A general search strategy in multilingual federated search environments is to translate the user query to each language of the information sources and run a monolingual search in each information source. It is then necessary to obtain a single ranked document list by merging the individual ranked lists from the information sources that are in different languages. This is known as the results merging problem for multilingual information retrieval. Previous research has shown that the simple approach of normalizing source-specific document scores is not effective. On the other side, a more effective merging method was proposed to download and translate all retrieved documents into the source language and generate the final ranked list by running a monolingual search in the search client. The latter method is more effective but is associated with a large amount of online communication and computation costs. This paper proposes an effective and efficient approach for the results merging task of multilingual ranked lists. Particularly, it downloads only a small number of documents from the individual ranked lists of each user query to calculate comparable document scores by utilizing both the query-based translation method and the document-based translation method. Then, query-specific and source-specific transformation models can be trained for individual ranked lists by using the information of these downloaded documents. These transformation models are used to estimate comparable document scores for all retrieved documents and thus the documents can be sorted into a final ranked list. This merging approach is efficient as only a subset of the retrieved documents are downloaded and translated online. Furthermore, an extensive set of experiments on the Cross-Language Evaluation Forum (CLEF) ( http://www.clef-campaign.org/ ) data has demonstrated the effectiveness of the query-specific and source-specific results merging algorithm against other alternatives. The new research in this paper proposes different variants of the query-specific and source-specific results merging algorithm with different transformation models. This paper also provides thorough experimental results as well as detailed analysis. All of the work substantially extends the preliminary research in (Si and Callan, in: Peters (ed.) Results of the cross-language evaluation forum-CLEF 2005, 2005).},
journal = {Inf. Retr.},
month = feb,
pages = {1–24},
numpages = {24},
keywords = {Multilingual information retrieval, Federated search, Results merging}
}

@article{10.1007/s10791-007-9035-7,
author = {Reforgiato Recupero, Diego},
title = {A New Unsupervised Method for Document Clustering by Using WordNet Lexical and Conceptual Relations},
year = {2007},
issue_date = {December  2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9035-7},
doi = {10.1007/s10791-007-9035-7},
abstract = {Text document clustering provides an effective and intuitive navigation mechanism to organize a large amount of retrieval results by grouping documents in a small number of meaningful classes. Many well-known methods of text clustering make use of a long list of words as vector space which is often unsatisfactory for a couple of reasons: first, it keeps the dimensionality of the data very high, and second, it ignores important relationships between terms like synonyms or antonyms. Our unsupervised method solves both problems by using ANNIE and WordNet lexical categories and WordNet ontology in order to create a well structured document vector space whose low dimensionality allows common clustering algorithms to perform well. For the clustering step we have chosen the bisecting  k -means and the Multipole tree, a modified version of the Antipole tree data structure for, respectively, their accuracy and speed.},
journal = {Inf. Retr.},
month = dec,
pages = {563–579},
numpages = {17},
keywords = {Antipole, Text documents, Clustering, Multipole, Bisecting k-means, WordNet}
}

@article{10.1007/s10791-007-9034-8,
author = {Diaz, Fernando},
title = {Regularizing Query-Based Retrieval Scores},
year = {2007},
issue_date = {December  2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9034-8},
doi = {10.1007/s10791-007-9034-8},
abstract = {We adapt the cluster hypothesis for score-based information retrieval by claiming that closely related documents should have similar scores. Given a retrieval from an arbitrary system, we describe an algorithm which directly optimizes this objective by adjusting retrieval scores so that topically related documents receive similar scores. We refer to this process as score regularization. Because score regularization operates on retrieval scores, regardless of their origin, we can apply the technique to arbitrary initial retrieval rankings. Document rankings derived from regularized scores, when compared to rankings derived from un-regularized scores, consistently and significantly result in improved performance given a variety of baseline retrieval algorithms. We also present several proofs demonstrating that regularization generalizes methods such as pseudo-relevance feedback, document expansion, and cluster-based retrieval. Because of these strong empirical and theoretical results, we argue for the adoption of score regularization as general design principle or post-processing step for information retrieval systems.},
journal = {Inf. Retr.},
month = dec,
pages = {531–562},
numpages = {32},
keywords = {Document expansion, Pseudo-relevance feedback, Cluster hypothesis, Query expansion, Cluster-based retrieval, Regularization}
}

@article{10.1007/s10791-007-9033-9,
author = {Savoy, Jacques},
title = {Searching Strategies for the Bulgarian Language},
year = {2007},
issue_date = {December  2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9033-9},
doi = {10.1007/s10791-007-9033-9},
abstract = {This paper reports on the underlying IR problems encountered when indexing and searching with the Bulgarian language. For this language we propose a general light stemmer and demonstrate that it can be quite effective, producing significantly better MAP (around + 34%) than an approach not applying stemming. We implement the GL2 model derived from the  Divergence from Randomness  paradigm and find its retrieval effectiveness better than other probabilistic, vector-space and language models. The resulting MAP is found to be about 50% better than the classical  tf idf  approach. Moreover, increasing the query size enhances the MAP by around 10% (from T to TD). In order to compare the retrieval effectiveness of our suggested stopword list and the light stemmer developed for the Bulgarian language, we conduct a set of experiments on another stopword list and also a more complex and aggressive stemmer. Results tend to indicate that there is no statistically significant difference between these variants and our suggested approach. This paper evaluates other indexing strategies such as 4-gram indexing and indexing based on the automatic decompounding of compound words. Finally, we analyze certain queries to discover why we obtained poor results, when indexing Bulgarian documents using the suggested word-based approach.},
journal = {Inf. Retr.},
month = dec,
pages = {509–529},
numpages = {21},
keywords = {Cross-language information retrieval, Stemmer, Bulgarian IR, Morphology, Evaluation}
}

@article{10.1007/s10791-007-9032-x,
author = {Buckley, Chris and Dimmick, Darrin and Soboroff, Ian and Voorhees, Ellen},
title = {Bias and the Limits of Pooling for Large Collections},
year = {2007},
issue_date = {December  2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9032-x},
doi = {10.1007/s10791-007-9032-x},
abstract = {Modern retrieval test collections are built through a process called pooling in which only a sample of the entire document set is judged for each topic. The idea behind pooling is to find enough relevant documents such that when unjudged documents are assumed to be nonrelevant the resulting judgment set is sufficiently complete and unbiased. Yet a constant-size pool represents an increasingly small percentage of the document set as document sets grow larger, and at some point the assumption of approximately complete judgments must become invalid. This paper shows that the judgment sets produced by traditional pooling when the pools are too small relative to the total document set size can be biased in that they favor relevant documents that contain topic title words. This phenomenon is wholly dependent on the collection size and does not depend on the number of relevant documents for a given topic. We show that the AQUAINT test collection constructed in the recent TREC 2005 workshop exhibits this biased relevance set; it is likely that the test collections based on the much larger GOV2 document set also exhibit the bias. The paper concludes with suggested modifications to traditional pooling and evaluation methodology that may allow very large reusable test collections to be built.},
journal = {Inf. Retr.},
month = dec,
pages = {491–508},
numpages = {18},
keywords = {Sampling bias, Test collections, Pooling}
}

@article{10.1007/s10791-007-9031-y,
author = {Yan, Rong and Hauptmann, Alexander G.},
title = {A Review of Text and Image Retrieval Approaches for Broadcast News Video},
year = {2007},
issue_date = {October   2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {4–5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9031-y},
doi = {10.1007/s10791-007-9031-y},
abstract = {The effectiveness of a video retrieval system largely depends on the choice of underlying text and image retrieval components. The unique properties of video collections (e.g., multiple sources, noisy features and temporal relations) suggest we examine the performance of these retrieval methods in such a multimodal environment, and identify the relative importance of the underlying retrieval components. In this paper, we review a variety of text/image retrieval approaches as well as their individual components in the context of broadcast news video. Numerous components of text/image retrieval have been discussed in detail, including retrieval models, text sources, temporal expansion methods, query expansion methods, image features, and similarity measures. For each component, we conduct a series of retrieval experiments on TRECVID video collections to identify their advantages and disadvantages. To provide a more complete coverage of video retrieval, we briefly discuss an emerging approach called concept-based video retrieval, and review strategies for combining multiple retrieval outputs.},
journal = {Inf. Retr.},
month = oct,
pages = {445–484},
numpages = {40},
keywords = {Fusion, Image retrieval, Concept-based retrieval, Video retrieval, Review, Text retrieval}
}

@article{10.1007/s10791-007-9030-z,
author = {Kettunen, Kimmo and Airio, Eija and J\"{a}rvelin, Kalervo},
title = {Restricted Inflectional Form Generation in Management of Morphological Keyword Variation},
year = {2007},
issue_date = {October   2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {4–5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9030-z},
doi = {10.1007/s10791-007-9030-z},
abstract = {Word form normalization through lemmatization or stemming is a standard procedure in information retrieval because morphological variation needs to be accounted for and several languages are morphologically non-trivial. Lemmatization is effective but often requires expensive resources. Stemming is also effective in most contexts, generally almost as good as lemmatization and typically much less expensive; besides it also has a query expansion effect. However, in both approaches the idea is to turn many inflectional word forms to a single lemma or stem both in the database index and in queries. This means extra effort in creating database indexes. In this paper we take an opposite approach: we leave the database index un-normalized and enrich the queries to cover for surface form variation of keywords. A potential penalty of the approach would be long queries and slow processing. However, we show that it only matters to cover a negligible number of possible surface forms even in morphologically complex languages to arrive at a performance that is almost as good as that delivered by stemming or lemmatization. Moreover, we show that, at least for typical test collections, it only matters to cover nouns and adjectives in queries. Furthermore, we show that our findings are particularly good for short queries that resemble normal searches of web users. Our approach is called FCG (for Frequent Case (form) Generation). It can be relatively easily implemented for Latin/Greek/Cyrillic alphabet languages by examining their (typically very skewed) nominal form statistics in a small text sample and by creating surface form generators for the 3---9 most frequent forms. We demonstrate the potential of our FCG approach for several languages of varying morphological complexity: Swedish, German, Russian, and Finnish in well-known test collections. Applications include in particular Web IR in languages poor in morphological resources.},
journal = {Inf. Retr.},
month = oct,
pages = {415–444},
numpages = {30},
keywords = {Inflected indexes, Best-match IR, Frequent case form generation for keywords, Generative methods in management of keyword variation}
}

@article{10.1007/s10791-007-9029-5,
author = {Lin, Jimmy and Wilbur, W. John},
title = {Syntactic Sentence Compression in the Biomedical Domain: Facilitating Access to Related Articles},
year = {2007},
issue_date = {October   2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {4–5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9029-5},
doi = {10.1007/s10791-007-9029-5},
abstract = {We explore a syntactic approach to sentence compression in the biomedical domain, grounded in the context of result presentation for related article search in the PubMed search engine. By automatically trimming inessential fragments of article titles, a system can effectively display more results in the same amount of space. Our implemented prototype operates by applying a sequence of syntactic trimming rules over the parse trees of article titles. Two separate studies were conducted using a corpus of manually compressed examples from MEDLINE: an automatic evaluation using B  leu  and a summative evaluation involving human assessors. Experiments show that a syntactic approach to sentence compression is effective in the biomedical domain and that the presentation of compressed article titles supports accurate "interest judgments", decisions by users as to whether an article is worth examining in more detail.},
journal = {Inf. Retr.},
month = oct,
pages = {393–414},
numpages = {22},
keywords = {MEDLINE, PubMed, Extrinsic evaluation, Genomics IR, Sentence compression}
}

@article{10.1007/s10791-007-9028-6,
author = {Zhang, Li and Li, Tao and Liu, Shixia and Pan, Yue},
title = {An Integrated System for Building Enterprise Taxonomies},
year = {2007},
issue_date = {October   2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {4–5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9028-6},
doi = {10.1007/s10791-007-9028-6},
abstract = {Although considerable research has been conducted in the field of hierarchical text categorization, little has been done on automatically collecting labeled corpus for building hierarchical taxonomies. In this paper, we propose an automatic method of collecting training samples to build hierarchical taxonomies. In our method, the category node is initially defined by some keywords, the web search engine is then used to construct a small set of labeled documents, and a topic tracking algorithm with keyword-based content normalization is applied to enlarge the training corpus on the basis of the seed documents. We also design a method to check the consistency of the collected corpus. The above steps produce a flat category structure which contains all the categories for building the hierarchical taxonomy. Next, linear discriminant projection approach is utilized to construct more meaningful intermediate levels of hierarchies in the generated flat set of categories. Experimental results show that the training corpus is good enough for statistical classification methods.},
journal = {Inf. Retr.},
month = oct,
pages = {365–391},
numpages = {27},
keywords = {Discriminant projection, Taxonomy, Consistency}
}

@article{10.1007/s10791-007-9027-7,
author = {Jiang, Jing and Zhai, Chengxiang},
title = {An Empirical Study of Tokenization Strategies for Biomedical Information Retrieval},
year = {2007},
issue_date = {October   2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {4–5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9027-7},
doi = {10.1007/s10791-007-9027-7},
abstract = {Due to the great variation of biological names in biomedical text, appropriate tokenization is an important preprocessing step for biomedical information retrieval. Despite its importance, there has been little study on the evaluation of various tokenization strategies for biomedical text. In this work, we conducted a careful, systematic evaluation of a set of tokenization heuristics on all the available TREC biomedical text collections for ad hoc document retrieval, using two representative retrieval methods and a pseudo-relevance feedback method. We also studied the effect of stemming and stop word removal on the retrieval performance. As expected, our experiment results show that tokenization can significantly affect the retrieval accuracy; appropriate tokenization can improve the performance by up to 96%, measured by mean average precision (MAP). In particular, it is shown that different query types require different tokenization heuristics, stemming is effective only for certain queries, and stop word removal in general does not improve the retrieval performance on biomedical text.},
journal = {Inf. Retr.},
month = oct,
pages = {341–363},
numpages = {23},
keywords = {Stop word, Biomedical information retrieval, Tokenization, Stemming}
}

@article{10.1007/s10791-007-9026-8,
author = {Kantor, Paul B.},
title = {Keith van Rijsbergen, The Geometry of Information Retrieval},
year = {2007},
issue_date = {October   2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {4–5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9026-8},
doi = {10.1007/s10791-007-9026-8},
journal = {Inf. Retr.},
month = oct,
pages = {485–489},
numpages = {5}
}

@article{10.1007/s10791-007-9025-9,
author = {Robertson, Stephen and Zaragoza, Hugo},
title = {On Rank-Based Effectiveness Measures and Optimization},
year = {2007},
issue_date = {June      2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9025-9},
doi = {10.1007/s10791-007-9025-9},
abstract = {Many current retrieval models and scoring functions contain free parameters which need to be set--ideally, optimized. The process of optimization normally involves some training corpus of the usual document-query-relevance judgement type, and some choice of measure that is to be optimized. The paper proposes a way to think about the process of exploring the space of parameter values, and how moving around in this space might be expected to affect different measures. One result, concerning local optima, is demonstrated for a range of rank-based evaluation measures.},
journal = {Inf. Retr.},
month = jun,
pages = {321–339},
numpages = {19},
keywords = {Effectiveness metrics, Ranking functions, Optimization}
}

@article{10.1007/s10791-007-9023-y,
author = {Wu, Shengli and McClean, Sally},
title = {Result Merging Methods in Distributed Information Retrieval with Overlapping Databases},
year = {2007},
issue_date = {June      2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9023-y},
doi = {10.1007/s10791-007-9023-y},
abstract = {In distributed information retrieval systems, document overlaps occur frequently among different component databases. This paper presents an experimental investigation and evaluation of a group of result merging methods including the shadow document method and the multi-evidence method in the environment of overlapping databases. We assume, with the exception of resultant document lists (either with rankings or scores), no extra information about retrieval servers and text databases is available, which is the usual case for many applications on the Internet and the Web.The experimental results show that the shadow document method and the multi-evidence method are the two best methods when overlap is high, while Round-robin is the best for low overlap. The experiments also show that [0,1] linear normalization is a better option than linear regression normalization for result merging in a heterogeneous environment.},
journal = {Inf. Retr.},
month = jun,
pages = {297–319},
numpages = {23},
keywords = {Distributed information retrieval, Overlapping databases, Result merging}
}

@article{10.1007/s10791-006-9021-5,
author = {Tanaka-Ishii, Kumiko and Ishii, Yuichiro},
title = {Multilingual Phrase-Based Concordance Generation in Real-Time},
year = {2007},
issue_date = {June      2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9021-5},
doi = {10.1007/s10791-006-9021-5},
abstract = {We present software that generates phrase-based concordances in real-time based on Internet searching. When a user enters a string of words for which he wants to find concordances, the system sends this string as a query to a search engine and obtains search results for the string. The concordances are extracted by performing statistical analysis on search results and then fed back to the user. Unlike existing tools, this concordance consultation tool is language-independent, so concordances can be obtained even in a language for which there are no well-established analytical methods. Our evaluation has revealed that concordances can be obtained more effectively than by only using a search engine directly.},
journal = {Inf. Retr.},
month = jun,
pages = {275–295},
numpages = {21},
keywords = {Concordance generation, Multilingual text analysis methods, Text mining}
}

@article{10.1007/s10791-006-9019-z,
author = {Metzler, Donald and Bruce Croft, W.},
title = {Linear Feature-Based Models for Information Retrieval},
year = {2007},
issue_date = {June      2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9019-z},
doi = {10.1007/s10791-006-9019-z},
abstract = {There have been a number of linear, feature-based models proposed by the information retrieval community recently. Although each model is presented differently, they all share a common underlying framework. In this paper, we explore and discuss the theoretical issues of this framework, including a novel look at the parameter space. We then detail supervised training algorithms that directly maximize the evaluation metric under consideration, such as mean average precision. We present results that show training models in this way can lead to significantly better test set performance compared to other training methods that do not directly maximize the metric. Finally, we show that linear feature-based models can consistently and significantly outperform current state of the art retrieval models with the correct choice of features.},
journal = {Inf. Retr.},
month = jun,
pages = {257–274},
numpages = {18},
keywords = {Linear models, Direct maximization, Retrieval models, Features}
}

@article{10.1007/s10791-006-9017-1,
author = {Amini, Massih R. and Tombros, Anastasios and Usunier, Nicolas and Lalmas, Mounia},
title = {Learning-Based Summarisation of XML Documents},
year = {2007},
issue_date = {June      2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9017-1},
doi = {10.1007/s10791-006-9017-1},
abstract = {Documents formatted in eXtensible Markup Language (XML) are available in collections of various document types. In this paper, we present an approach for the summarisation of XML documents. The novelty of this approach lies in that it is based on features not only from the content of documents, but also from their logical structure. We follow a machine learning, sentence extraction-based summarisation technique. To find which features are more effective for producing summaries, this approach views sentence extraction as an ordering task. We evaluated our summarisation model using the INEX and SUMMAC datasets. The results demonstrate that the inclusion of features from the logical structure of documents increases the effectiveness of the summariser, and that the learnable system is also effective and well-suited to the task of summarisation in the context of XML documents. Our approach is generic, and is therefore applicable, apart from entire documents, to elements of varying granularity within the XML tree. We view these results as a step towards the intelligent summarisation of XML documents.},
journal = {Inf. Retr.},
month = jun,
pages = {233–255},
numpages = {23},
keywords = {XML documents, Ranking functions, Text summarisation, Machine learning}
}

@article{10.1007/s10791-006-9014-4,
author = {Moffat, Alistair and Webber, William and Zobel, Justin and Baeza-Yates, Ricardo},
title = {A Pipelined Architecture for Distributed Text Query Evaluation},
year = {2007},
issue_date = {June      2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9014-4},
doi = {10.1007/s10791-006-9014-4},
abstract = {Two principal query-evaluation methodologies have been described for cluster-based implementation of distributed information retrieval systems: document partitioning and term partitioning. In a document-partitioned system, each of the processors hosts a subset of the documents in the collection, and executes every query against its local sub-collection. In a term-partitioned system, each of the processors hosts a subset of the inverted lists that make up the index of the collection, and serves them to a central machine as they are required for query evaluation.In this paper we introduce a pipelined query-evaluation methodology, based on a term-partitioned index, in which partially evaluated queries are passed amongst the set of processors that host the query terms. This arrangement retains the disk read benefits of term partitioning, but more effectively shares the computational load. We compare the three methodologies experimentally, and show that term distribution is inefficient and scales poorly. The new pipelined approach offers efficient memory utilization and efficient use of disk accesses, but suffers from problems with load balancing between nodes. Until these problems are resolved, document partitioning remains the preferred method.},
journal = {Inf. Retr.},
month = jun,
pages = {205–231},
numpages = {27},
keywords = {Distributed retrieval, Text searching, Index representations}
}

@article{10.1007/s10791-007-9024-x,
author = {Jung, Jason J.},
title = {Ontological Framework Based on Contextual Mediation for Collaborative Information Retrieval},
year = {2007},
issue_date = {April     2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9024-x},
doi = {10.1007/s10791-007-9024-x},
journal = {Inf. Retr.},
month = apr,
pages = {203},
numpages = {1}
}

@article{10.1007/s10791-007-9022-z,
author = {Crestani, Fabio and Ruthven, Ian},
title = {Introduction to Special Issue on Contextual Information Retrieval Systems},
year = {2007},
issue_date = {April     2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-007-9022-z},
doi = {10.1007/s10791-007-9022-z},
journal = {Inf. Retr.},
month = apr,
pages = {111–113},
numpages = {3}
}

@article{10.1007/s10791-006-9020-6,
author = {Liu, Zhenyu and Chu, Wesley W.},
title = {Knowledge-Based Query Expansion to Support Scenario-Specific Retrieval of Medical Free Text},
year = {2007},
issue_date = {April     2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9020-6},
doi = {10.1007/s10791-006-9020-6},
abstract = {In retrieving medical free text, users are often interested in answers pertinent to certain scenarios that correspond to common tasks performed in medical practice, e.g.,  treatment  or  diagnosis  of a disease. A major challenge in handling such queries is that scenario terms in the query (e.g.,  treatment ) are often too general to match specialized terms in relevant documents (e.g.,  chemotherapy ). In this paper, we propose a knowledge-based query expansion method that exploits the UMLS knowledge source to append the original query with additional terms that are specifically relevant to the query's scenario(s). We compared the proposed method with traditional statistical expansion that expands terms which are statistically correlated but not necessarily scenario specific. Our study on two standard testbeds shows that the knowledge-based method, by providing scenario-specific expansion, yields notable improvements over the statistical method in terms of average precision-recall. On the OHSUMED testbed, for example, the improvement is more than 5% averaging over all scenario-specific queries studied and about 10% for queries that mention certain scenarios, such as  treatment of a disease  and  differential diagnosis of a symptom/disease .},
journal = {Inf. Retr.},
month = apr,
pages = {173–202},
numpages = {30},
keywords = {Knowledge-based systems, Medical information retrieval, Scenario-specific information retrieval, Knowledge-based information retrieval, Query expansion}
}

@article{10.1007/s10791-006-9018-0,
author = {Hernandez, Nathalie and Mothe, Josiane and Chrisment, Claude and Egret, Daniel},
title = {Modeling Context through Domain Ontologies},
year = {2007},
issue_date = {April     2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9018-0},
doi = {10.1007/s10791-006-9018-0},
abstract = {Traditional information retrieval systems aim at satisfying most users for most of their searches, leaving aside the context in which the search takes place. We propose to model two main aspects of context: The themes of the user's information need and the specific data the user is looking for to achieve the task that has motivated his search. Both aspects are modeled by means of ontologies. Documents are semantically indexed according to the context representation and the user accesses information by browsing the ontologies. The model has been applied to a case study that has shown the added value of such a semantic representation of context.},
journal = {Inf. Retr.},
month = apr,
pages = {143–172},
numpages = {30},
keywords = {Task, Ontology, Semantic indexing, Document representation, Browsing interface}
}

@article{10.1007/s10791-006-9016-2,
author = {Campbell, D. R. and Culley, S. J. and Mcmahon, C. A. and Sellini, F.},
title = {An Approach for the Capture of Context-Dependent Document Relationships Extracted from Bayesian Analysis of Users' Interactions with Information},
year = {2007},
issue_date = {April     2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9016-2},
doi = {10.1007/s10791-006-9016-2},
abstract = {A number of technologies exist which enable the unobtrusive capture of computer interface interactions in the background of a user's working environment. The resulting data can be used in a variety of ways to model aspects of search activity and the general use of electronic documents in normal working routines. In this paper we present an approach for using captured data to identify relationships between documents used by an individual or group, representing their value in a given context--that may relate to specific information need or activity. The approach employs the use of a na\"{\i}ve Bayesian classifier to evaluate possible relationships that are derived implicitly from the data. It is intended that the relationships established be stored within an information retrieval (IR) system to aid in the retrieval of related documents where future users arrive at a similar context. In the evaluation of the approach over 70 hours of data from computer users in industrial and academic settings are collected to assess its overall feasibility. The results indicate that the approach provides a useful method for the establishment of identifiable relationships between documents based on the context of their usage, rather than their content.},
journal = {Inf. Retr.},
month = apr,
pages = {115–141},
numpages = {27},
keywords = {Context, Recommender systems, Na\"{\i}ve Bayesian classification, Implicit indicators}
}

@article{10.1007/s10791-006-9013-5,
author = {Jung, Jason J.},
title = {Ontological Framework Based on Contextual Mediation for Collaborative Information Retrieval},
year = {2007},
issue_date = {January   2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9013-5},
doi = {10.1007/s10791-006-9013-5},
abstract = {On the heterogeneous web information spaces, users have been suffering from efficiently searching for relevant information. This paper proposes a mediator agent system to estimate the semantics of unknown web spaces by learning the fragments gathered during the users' focused crawling. This process is organized as the following three tasks; (i) gathering semantic information about web spaces from personal agents while focused crawling in unknown spaces, (ii) reorganizing the information by using ontology alignment algorithm, and (iii) providing relevant semantic information to personal agents right before focused crawling. It makes the personal agent possible to recognize the corresponding user's behaviors in semantically heterogeneous spaces and predict his searching contexts. For the experiments, we implemented comparison-shopping system with heterogeneous web spaces. As a result, our proposed method efficiently supported the users, and then, network traffic was also reduced.},
journal = {Inf. Retr.},
month = jan,
pages = {85–109},
numpages = {25},
keywords = {Multi-agent systems, Collaborative information retrieval, Feature manipulation, Focused crawling, Ontology, Mediation}
}

@article{10.1007/s10791-006-9012-6,
author = {Serrano, J. I. and Castillo, M. D.},
title = {Evolutionary Learning of Document Categories},
year = {2007},
issue_date = {January   2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9012-6},
doi = {10.1007/s10791-006-9012-6},
abstract = {This paper deals with a supervised learning method devoted to producing categorization models of text documents. The goal of the method is to use a suitable numerical measurement of example similarity to find centroids describing different categories of examples. The centroids are not abstract or statistical models, but rather consist of bits of examples. The centroid-learning method is based on a Genetic Algorithm for Texts (GAT). The categorization system using this genetic algorithm infers a model by applying the genetic algorithm to each set of preclassified documents belonging to a category. The models thus obtained are the category centroids that are used to predict the category of a test document. The experimental results validate the utility of this approach for classifying incoming documents.},
journal = {Inf. Retr.},
month = jan,
pages = {69–83},
numpages = {15},
keywords = {Genetic algorithms, Text categorization, Distance-based methods}
}

@article{10.1007/s10791-006-9004-6,
author = {Zelikovitz, Sarah and Cohen, William W. and Hirsh, Haym},
title = {Extending WHIRL with Background Knowledge for Improved Text Classification},
year = {2007},
issue_date = {January   2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9004-6},
doi = {10.1007/s10791-006-9004-6},
abstract = {Intelligent use of the many diverse forms of data available on the Internet requires new tools for managing and manipulating heterogeneous forms of information. This paper uses WHIRL, an extension of relational databases that can manipulate textual data using statistical similarity measures developed by the information retrieval community. We show that although WHIRL is designed for more general similarity-based reasoning tasks, it is competitive with mature systems designed explicitly for inductive classification. In particular, WHIRL is well suited for combining different sources of knowledge in the classification process. We show on a diverse set of tasks that the use of appropriate sets of unlabeled background knowledge often decreases error rates, particularly if the number of examples or the size of the strings in the training set is small. This is especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a particular problem on the World Wide Web.},
journal = {Inf. Retr.},
month = jan,
pages = {35–67},
numpages = {33},
keywords = {Background knowledge, Information retrieval, Query processing, Semi-supervised learning, Text categorization}
}

@article{10.1007/s10791-006-9001-9,
author = {Brisaboa, Nieves R. and Fari\~{n}a, Antonio and Navarro, Gonzalo and Param\'{a}, Jos\'{e} R.},
title = {Lightweight Natural Language Text Compression},
year = {2007},
issue_date = {January   2007},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {10},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9001-9},
doi = {10.1007/s10791-006-9001-9},
abstract = {Variants of Huffman codes where words are taken as the source symbols are currently the most attractive choices to compress natural language text databases. In particular, Tagged Huffman Code by Moura et al. offers fast direct searching on the compressed text and random access capabilities, in exchange for producing around 11% larger compressed files. This work describes End-Tagged Dense Code and (  s ,  c )-Dense Code, two new semistatic statistical methods for compressing natural language texts. These techniques permit simpler and faster encoding and obtain better compression ratios than Tagged Huffman Code, while maintaining its fast direct search and random access capabilities. We show that Dense Codes improve Tagged Huffman Code compression ratio by about 10%, reaching only 0.6% overhead over the optimal Huffman compression ratio. Being simpler, Dense Codes are generated 45% to 60% faster than Huffman codes. This makes Dense Codes a very attractive alternative to Huffman code variants for various reasons: they are simpler to program, faster to build, of almost optimal size, and as fast and easy to search as the best Huffman variants, which are not so close to the optimal size.},
journal = {Inf. Retr.},
month = jan,
pages = {1–33},
numpages = {33},
keywords = {Searching compressed text, Natural language text compression, Text databases}
}

@article{10.1007/s10791-006-9011-7,
author = {Rukoz, Marta and Manouvrier, Maude and Jomier, Genevi\`{e}ve},
title = {Δ-Distance: A Family of Dissimilarity Metrics between Images Represented by Multi-Level Feature Vectors},
year = {2006},
issue_date = {December  2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9011-7},
doi = {10.1007/s10791-006-9011-7},
abstract = {This article presents the -distance, a family of distances between images recursively decomposed into segments and represented by multi-level feature vectors. Such a structure is a quad, a quin or a nona-tree resulting from a fixed and arbitrary image partition or from an image segmentation process. It handles positional information of image features (e.g. color, texture or shape). -distance is the generalized form of dissimilarity measures between multi-level feature vectors. Using different weights on tree nodes and different distances between nodes, distances between trees or visual similarity between images can be computed based on the general definition of . In this article, we present three -based distance families: two families of distances between tree structures, called  $$mathcal{T}$$   -distance (  $$mathcal{T}$$  for  Tree ) and  $$mathcal{S}$$   -distance  (  $$mathcal{S}$$  for  Segment ), and a family of visual distances between images, called (  $$mathcal{V}$$  for  Visual ). The  $$mathcal{V}$$ -distance visually compares two images using their tree representation and the other two distances compare the tree structures resulting from image segmentation. Moreover, we show how existing distances between multi-level feature vectors appear to be particular cases of the -distance},
journal = {Inf. Retr.},
month = dec,
pages = {633–655},
numpages = {23},
keywords = {Image database, Similarity, Content-based image retrieval, Distance between quad/quin or nona-trees, Similarity of image segments}
}

@article{10.1007/s10791-006-9010-8,
author = {Lau, Gloria T. and Law, Kincho H. and Wiederhold, Gio},
title = {A Relatedness Analysis of Government Regulations Using Domain Knowledge and Structural Organization},
year = {2006},
issue_date = {December  2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9010-8},
doi = {10.1007/s10791-006-9010-8},
abstract = {The complexity and diversity of government regulations make understanding and retrieval of regulations a non-trivial task. One of the issues is the existence of multiple sources of regulations and interpretive guides with differences in format, terminology and context. This paper describes a comparative analysis scheme developed to help retrieval of related provisions from different regulatory documents. Specifically, the goal is to identify the most strongly related provisions between regulations. The relatedness analysis makes use of not only traditional term match but also a combination of feature matches, and not only content comparison but also structural analysis.Regulations are first compared based on conceptual information as well as domain knowledge through feature matching. Regulations also possess specific organizational structures, such as a tree hierarchy of provisions and heavy referencing between provisions. These structures represent useful information in locating related provisions, and are therefore exploited in the comparison of regulations for completeness. System performance is evaluated by comparing a similarity ranking produced by users with the machine-predicted ranking. Ranking produced by the relatedness analysis system shows a reduction in error compared to that of Latent Semantic Indexing. Various pairs of regulations are compared and the results are analyzed along with observations based on different feature usages. An example of an e-rulemaking scenario is shown to demonstrate capabilities and limitations of the prototype relatedness analysis system.},
journal = {Inf. Retr.},
month = dec,
pages = {657–680},
numpages = {24},
keywords = {E-rulemaking, Structural analysis, Feature matching, Relatedness analysis}
}

@article{10.1007/s10791-006-9009-1,
author = {Ahlgren, Per and Kek\"{a}l\"{a}inen, Jaana},
title = {Swedish Full Text Retrieval: Effectiveness of Different Combinations of Indexing Strategies with Query Terms},
year = {2006},
issue_date = {December  2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9009-1},
doi = {10.1007/s10791-006-9009-1},
abstract = {In this paper, which treats Swedish full text retrieval, the problem of morphological variation of query terms in the document database is studied. The Swedish CLEF 2003 test collection was used, and the effects of combination of indexing strategies with query terms on retrieval effectiveness were studied. Four of the seven tested combinations involved indexing strategies that used normalization, a form of conflation. All of these four combinations employed compound splitting, both during indexing and at query phase. SWETWOL, a morphological analyzer for the Swedish language, was used for normalization and compound splitting. A fifth combination used stemming, while a sixth attempted to group related terms by right hand truncation of query terms. The truncation was performed by a search expert. These six combinations were compared to each other and to a baseline combination, where no attempt was made to counteract the problem of morphological variation of query terms in the document database. Both the truncation combination, the four combinations based on normalization and the stemming combination outperformed the baseline. Truncation had the best performance. The main conclusion of the paper is that truncation, normalization and stemming enhanced retrieval effectiveness in comparison to the baseline. Further, normalization and stemming were not far below truncation.},
journal = {Inf. Retr.},
month = dec,
pages = {681–697},
numpages = {17},
keywords = {Truncation, Indexing strategies, Swedish, Morphological analysis, Stemming}
}

@article{10.1007/s10791-006-9008-2,
author = {G\"{o}vert, Norbert and Fuhr, Norbert and Lalmas, Mounia and Kazai, Gabriella},
title = {Evaluating the Effectiveness of Content-Oriented XML Retrieval Methods},
year = {2006},
issue_date = {December  2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9008-2},
doi = {10.1007/s10791-006-9008-2},
abstract = {Content-oriented XML retrieval approaches aim at a more focused retrieval strategy: Instead of retrieving whole documents, document components that are exhaustive to the information need while at the same time being as specific as possible should be retrieved. In this article, we show that the evaluation methods developed for standard retrieval must be modified in order to deal with the structure of XML documents. More precisely, the size and overlap of document components must be taken into account. For this purpose, we propose a new effectiveness metric based on the definition of a concept space defined upon the notions of exhaustiveness and specificity of a search result. We compare the results of this new metric by the results obtained with the official metric used in INEX, the evaluation initiative for content-oriented XML retrieval.},
journal = {Inf. Retr.},
month = dec,
pages = {699–722},
numpages = {24},
keywords = {Exhaustiveness and specificity, Metrics, Effectiveness, XML retrieval, Evaluation}
}

@article{10.1007/s10791-006-9006-4,
author = {Cronen-Townsend, Steve and Zhou, Yun and Croft, W. Bruce},
title = {Precision Prediction Based on Ranked List Coherence},
year = {2006},
issue_date = {December  2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9006-4},
doi = {10.1007/s10791-006-9006-4},
abstract = {We introduce a statistical measure of the coherence of a list of documents called the  clarity score . Starting with a document list ranked by the query-likelihood retrieval model, we demonstrate the score's relationship to query ambiguity with respect to the collection. We also show that the clarity score is correlated with the average precision of a query and lay the groundwork for useful predictions by discussing a method of setting decision thresholds automatically. We then show that passage-based clarity scores correlate with average-precision measures of ranked lists of passages, where a passage is judged relevant if it contains correct answer text, which extends the basic method to passage-based systems. Next, we introduce variants of document-based clarity scores to improve the robustness, applicability, and predictive ability of clarity scores. In particular, we introduce the ranked list clarity score that can be computed with only a ranked list of documents, and the weighted clarity score where query terms contribute more than other terms. Finally, we show an approach to predicting queries that perform poorly on query expansion that uses techniques expanding on the ideas presented earlier.},
journal = {Inf. Retr.},
month = dec,
pages = {723–755},
numpages = {33},
keywords = {Performance prediction, Query expansion, Language models}
}

@article{10.1007/s10791-006-9007-3,
author = {Kang, In-Su and Na, Seung-Hoon and Lee, Jong-Hyeok},
title = {Collection-Based Compound Noun Segmentation for Korean Information Retrieval},
year = {2006},
issue_date = {November  2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9007-3},
doi = {10.1007/s10791-006-9007-3},
abstract = {Compound noun segmentation is a key first step in language processing for Korean. Thus far, most approaches require some form of human supervision, such as pre-existing dictionaries, segmented compound nouns, or heuristic rules. As a result, they suffer from the unknown word problem, which can be overcome by unsupervised approaches. However, previous unsupervised methods normally do not consider all possible segmentation candidates, and/or rely on character-based segmentation clues such as bi-grams or all-length  n -grams. So, they are prone to falling into a local solution. To overcome the problem, this paper proposes an unsupervised segmentation algorithm that searches the most likely segmentation result from all possible segmentation candidates using a word-based segmentation context. As word-based segmentation clues, a dictionary is automatically generated from a corpus. Experiments using three test collections show that our segmentation algorithm is successfully applied to Korean information retrieval, improving a dictionary-based longest-matching algorithm.},
journal = {Inf. Retr.},
month = nov,
pages = {613–631},
numpages = {19},
keywords = {Compound noun segmentation, Unsupervised method, Korean information retrieval}
}

@article{10.1007/s10791-006-9005-5,
author = {Wei, Xing and Croft, Bruce and Mccallum, Andrew},
title = {Table Extraction for Answer Retrieval},
year = {2006},
issue_date = {November  2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9005-5},
doi = {10.1007/s10791-006-9005-5},
abstract = {The ability to find tables and extract information from them is a necessary component of many information retrieval tasks. Documents often contain tables in order to communicate densely packed, multi-dimensional information. Tables do this by employing layout patterns to efficiently indicate fields and records in two-dimensional form. Their rich combination of formatting and content presents difficulties for traditional retrieval techniques. This paper describes techniques for extracting tables from text and retrieving answers from the extracted information. We compare machine learning (especially, Conditional Random Fields) and heuristic methods for table extraction. To retrieve answers, our approach creates a cell document, which contains the cell and its metadata (headers, titles) for each table cell, and the retrieval model ranks the cells of the extracted tables using a language-modeling approach. Performance is tested using government statistical Web sites and news articles, and errors are analyzed in order to improve the system.},
journal = {Inf. Retr.},
month = nov,
pages = {589–611},
numpages = {23},
keywords = {Conditional random fields, Table extraction, Information extraction, Question answering}
}

@article{10.1007/s10791-006-9003-7,
author = {Lin, Jimmy and Demner-Fushman, Dina},
title = {Methods for Automatically Evaluating Answers to Complex Questions},
year = {2006},
issue_date = {November  2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9003-7},
doi = {10.1007/s10791-006-9003-7},
abstract = {Evaluation is a major driving force in advancing the state of the art in language technologies. In particular, methods for automatically assessing the quality of machine output is the preferred method for measuring progress, provided that these metrics have been validated against human judgments. Following recent developments in the automatic evaluation of machine translation and document summarization, we present a similar approach, implemented in a measure called P  OURPRE , an automatic technique for evaluating answers to complex questions based on  n -gram co-occurrences between machine output and a human-generated answer key. Until now, the only way to assess the correctness of answers to such questions involves manual determination of whether an information "nugget" appears in a system's response. The lack of automatic methods for scoring system output is an impediment to progress in the field, which we address with this work. Experiments with the TREC 2003, TREC 2004, and TREC 2005 QA tracks indicate that rankings produced by our metric correlate highly with official rankings, and that P  OURPRE  outperforms direct application of existing metrics.},
journal = {Inf. Retr.},
month = nov,
pages = {565–587},
numpages = {23},
keywords = {Evaluation, Question answering}
}

@article{10.1007/s10791-006-9002-8,
author = {Wilbur, W. John and Kim, Won and Xie, Natalie},
title = {Spelling Correction in the PubMed Search Engine},
year = {2006},
issue_date = {November  2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9002-8},
doi = {10.1007/s10791-006-9002-8},
abstract = {It is known that users of internet search engines often enter queries with misspellings in one or more search terms. Several web search engines make suggestions for correcting misspelled words, but the methods used are proprietary and unpublished to our knowledge. Here we describe the methodology we have developed to perform spelling correction for the PubMed search engine. Our approach is based on the noisy channel model for spelling correction and makes use of statistics harvested from user logs to estimate the probabilities of different types of edits that lead to misspellings. The unique problems encountered in correcting search engine queries are discussed and our solutions are outlined.},
journal = {Inf. Retr.},
month = nov,
pages = {543–564},
numpages = {22},
keywords = {Noisy channel model, Nonword error detection, Edit distance, User query logs, Trie}
}

@article{10.1007/s10791-006-9000-x,
author = {Zhao, Le and Zhang, Min and Ma, Shaoping},
title = {The Nature of Novelty Detection},
year = {2006},
issue_date = {November  2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9000-x},
doi = {10.1007/s10791-006-9000-x},
abstract = {Sentence level novelty detection aims at spotting sentences with novel information from an ordered sentence list. In the task, sentences appearing later in the list with no new meanings are eliminated. For the task of novelty detection, the contributions of this paper are three-fold. First, conceptually, this paper reveals the computational nature of the task currently overlooked by the Novelty community--Novelty as a combination of partial overlap (PO) and complete overlap (CO) relations between sentences. We define partial overlap between two sentences as a sharing of common facts, while complete overlap is when one sentence covers all of the meanings of the other sentence. Second, technically, a novel approach, the selected pool method is provided which follows naturally from the PO-CO computational structure. We provide formal error analysis for selected pool and methods based on this PO-CO framework. We address the question how accurate must the PO judgments be to outperform the baseline pool method. Third, experimentally, results were presented for all the three novelty datasets currently available. Results show that the selected pool is significantly better or no worse than the current methods, an indication that the term overlap criterion for the PO judgments could be adequately accurate.},
journal = {Inf. Retr.},
month = nov,
pages = {521–541},
numpages = {21},
keywords = {TREC, Overlap relations, Meanings, Novelty detection}
}

@article{10.1007/s10791-006-9397-2,
author = {Losada, David E. and Fern\'{a}ndez-Luna, Juan M.},
title = {Introduction to the Special Issue on the 27th European Conference on Information Retrieval Research},
year = {2006},
issue_date = {September 2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-9397-2},
doi = {10.1007/s10791-006-9397-2},
journal = {Inf. Retr.},
month = sep,
pages = {395–397},
numpages = {3}
}

@article{10.1007/s10791-006-6614-y,
author = {Blanco, Roi and Barreiro, \'{A}lvaro},
title = {TSP and Cluster-Based Solutions to the Reassignment of Document Identifiers},
year = {2006},
issue_date = {September 2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-6614-y},
doi = {10.1007/s10791-006-6614-y},
abstract = {Recent studies demonstrated that it is possible to reduce  Inverted Files  (IF) sizes by reassigning the document identifiers of the original collection, as this lowers the distance between the positions of documents related to a single term. Variable-bit encoding schemes can exploit the average gap reduction and decrease the total amount of bits per document pointer. This paper presents an efficient solution to the reassignment problem, which consists in reducing the input data dimensionality using a SVD transformation, as well as considering it a  Travelling Salesman Problem  (TSP). We also present some efficient solutions based on clustering. Finally, we combine both the TSP and the clustering strategies for reordering the document identifiers. We present experimental tests and performance results in two text TREC collections, obtaining good compression ratios with low running times, and advance the possibility of obtaining scalable solutions for web collections based on the techniques presented here.},
journal = {Inf. Retr.},
month = sep,
pages = {499–517},
numpages = {19},
keywords = {Compression, Clustering, TSP, SVD, Indexing, Document identifier reassignment}
}

@article{10.1007/s10791-006-6391-7,
author = {Vinay, Vishwa and Cox, Ingemar J. and Milic-Frayling, Natasa and Wood, Ken},
title = {Can Constrained Relevance Feedback and Display Strategies Help Users Retrieve Items on Mobile Devices?},
year = {2006},
issue_date = {September 2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-6391-7},
doi = {10.1007/s10791-006-6391-7},
abstract = {Searching online information resources using mobile devices is affected by small screens which can display only a fraction of ranked search results. In this paper we investigate whether the search effort can be reduced by means of a simple user feedback: for a screenful of search results the user is encouraged to indicate a single most relevant document. In our approach we exploit the fact that, for small display sizes and limited user actions, we can construct a user decision tree representing all possible outcomes of the user interaction with the system. Examining the trees we can compute an upper limit on relevance feedback performance. In this study we consider three standard feedback algorithms: Rocchio, Robertson/Sparck-Jones (RSJ) and a Bayesian algorithm. We evaluate them in conjunction with two strategies for presenting search results: a document ranking that attempts to maximize information gain from the user's choices and the top-D ranked documents. Experimental results indicate that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For the other two algorithms, the exploratory display that maximizes information gain produces better results. We conducted a user study to compare the performance of the relevance feedback methods with real users and compare the results with the findings from the tree analysis. This comparison between the simulations and real user behaviour indicates that the Bayesian algorithm, coupled with the sampled display, is the most effective.},
journal = {Inf. Retr.},
month = sep,
pages = {435–453},
numpages = {19},
keywords = {Small displays, Relevance feedback, Display strategies}
}

@article{10.1007/s10791-006-6390-8,
author = {Vechtomova, Olga},
title = {Noun Phrases in Interactive Query Expansion and Document Ranking},
year = {2006},
issue_date = {September 2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-6390-8},
doi = {10.1007/s10791-006-6390-8},
abstract = {The paper presents several techniques for selecting noun phrases for interactive query expansion following pseudo-relevance feedback and a new phrase-based document ranking method. A combined syntactico-statistical method was used for the selection of phrases for query expansion. Several statistical measures of phrase selection were evaluated. Experiments were also conducted studying the effectiveness of noun phrases in document ranking. One of the major problems in phrase-based document retrieval is weighting of overlapping and non-contiguous word sequences in documents. The paper presents a new method of phrase weighting, which addressed this problem, and its evaluation on the TREC dataset.},
journal = {Inf. Retr.},
month = sep,
pages = {399–420},
numpages = {22},
keywords = {Document ranking, Interactive query expansion, Noun phrases}
}

@article{10.1007/s10791-006-6389-1,
author = {Lehtokangas, Raija and Keskustalo, Heikki and J\"{a}rvelin, Kalervo},
title = {Experiments with Dictionary-Based CLIR Using Graded Relevance Assessments: Improving Effectiveness by Pseudo-Relevance Feedback},
year = {2006},
issue_date = {September 2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-6389-1},
doi = {10.1007/s10791-006-6389-1},
abstract = {Research on cross-language information retrieval (CLIR) has typically been restricted to settings using binary relevance assessments. In this paper, we present evaluation results for dictionary-based CLIR using graded relevance assessments in a best match retrieval environment. A text database containing newspaper articles and a related set of 35 search topics were used in the tests. First, monolingual baseline queries were automatically formed from the topics. Secondly, source language topics (in English, German, and Swedish) were automatically translated into the target language (Finnish), using structured target queries. The effectiveness of the translated queries was compared to that of the monolingual queries. Thirdly, pseudo-relevance feedback was used to expand the original target queries. CLIR performance was evaluated using three relevance thresholds: stringent, regular, and liberal. When regular or liberal threshold was used, a reasonable performance was achieved. Using stringent threshold, equally high performance could not be achieved. On all the relevance thresholds the performance of the translated queries was successfully raised by pseudo-relevance feedback based query expansion. However, the performance of the stringent threshold in relation to the other thresholds could not be raised by this method.},
journal = {Inf. Retr.},
month = sep,
pages = {421–433},
numpages = {13},
keywords = {Relevance feedback, Cross-language information retrieval, Graded relevance assessments}
}

@article{10.1007/s10791-006-6388-2,
author = {Lu, Jie and Callan, Jamie},
title = {Full-Text Federated Search of Text-Based Digital Libraries in Peer-to-Peer Networks},
year = {2006},
issue_date = {September 2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-6388-2},
doi = {10.1007/s10791-006-6388-2},
abstract = {Peer-to-peer (P2P) networks integrate autonomous computing resources without requiring a central coordinating authority, which makes them a potentially robust and scalable model for providing federated search capability to large-scale networks of text-based digital libraries. However, peer-to-peer networks have so far provided very limited support for full-text federated search with relevance-based document ranking. This paper provides solutions to full-text federated search of text-based digital libraries in hierarchical peer-to-peer networks. Existing approaches to full-text search are adapted and new methods are developed for the problems of resource representation, resource selection, and result merging according to the unique characteristics of hierarchical peer-to-peer networks. Experimental results demonstrate that the proposed approaches offer a better combination of accuracy and efficiency than more common alternatives for federated search of text-based digital libraries in peer-to-peer networks.},
journal = {Inf. Retr.},
month = sep,
pages = {477–498},
numpages = {22},
keywords = {Peer-to-peer networks, Text-based digital libraries, Full-text, Federated search}
}

@article{10.1007/s10791-006-6387-3,
author = {Suomela, Sari and Kek\"{a}l\"{a}inen, Jaana},
title = {User Evaluation of Ontology as Query Construction Tool},
year = {2006},
issue_date = {September 2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-6387-3},
doi = {10.1007/s10791-006-6387-3},
abstract = {This study examines the use of an ontology as a search tool. Sixteen subjects created queries using Concept-based Information Retrieval Interface (CIRI) and a regular baseline IR interface. The simulated work task method was used to make the searching situations realistic. Subjects' search experiences, queries and search results were examined. The numbers of search concepts and keys, as well as their overlap in the queries were investigated. The effectiveness of the CIRI and baseline queries was compared. An Ontology Index (OI) was calculated for all search tasks and the correlation between the OI and the overlap of search concepts and keys in queries was investigated. The number of search keys and concepts was higher in CIRI queries than in baseline interface queries. Also the overlap of search keys was higher among CIRI users than among baseline users. These both findings are due to CIRI's expansion feature. There was no clear correlation between OI and overlap of search concepts and keys. The search results were evaluated with generalised precision and recall, and relevance scores based on individual relevance assessments. The baseline interface queries performed better in all comparisons, but the difference was statistically significant only in relevance scores based on individual relevance assessments.},
journal = {Inf. Retr.},
month = sep,
pages = {455–475},
numpages = {21},
keywords = {Ontologies, Interactive information retrieval, User evaluation}
}

@article{10.1007/s10791-006-8703-3,
author = {Desouza, Kevin C.},
title = {Review of Profiling Machines: Mapping the Personal Information Economy},
year = {2006},
issue_date = {June      2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-8703-3},
doi = {10.1007/s10791-006-8703-3},
journal = {Inf. Retr.},
month = jun,
pages = {387–389},
numpages = {3}
}

@article{10.1007/s10791-006-8702-4,
author = {Azzam, Saliha and Humphreys, Kevin},
title = {New Directions in Question Answering},
year = {2006},
issue_date = {June      2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-8702-4},
doi = {10.1007/s10791-006-8702-4},
journal = {Inf. Retr.},
month = jun,
pages = {383–386},
numpages = {4}
}

@article{10.1007/s10791-006-4651-1,
author = {Jin, Rong and Si, Luo and Zhai, Chengxiang},
title = {A Study of Mixture Models for Collaborative Filtering},
year = {2006},
issue_date = {June      2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-4651-1},
doi = {10.1007/s10791-006-4651-1},
abstract = {Collaborative filtering is a general technique for exploiting the preference patterns of a group of users to predict the utility of items for a particular user. Three different components need to be modeled in a collaborative filtering problem: users, items, and ratings. Previous research on applying probabilistic models to collaborative filtering has shown promising results. However, there is a lack of systematic studies of different ways to model each of the three components and their interactions. In this paper, we conduct a broad and systematic study on different mixture models for collaborative filtering. We discuss general issues related to using a mixture model for collaborative filtering, and propose three properties that a graphical model is expected to satisfy. Using these properties, we thoroughly examine five different mixture models, including Bayesian Clustering (BC), Aspect Model (AM), Flexible Mixture Model (FMM), Joint Mixture Model (JMM), and the Decoupled Model (DM). We compare these models both analytically and experimentally. Experiments over two datasets of movie ratings under different configurations show that in general, whether a model satisfies the proposed properties tends to be correlated with its performance. In particular, the Decoupled Model, which satisfies all the three desired properties, outperforms the other mixture models as well as many other existing approaches for collaborative filtering. Our study shows that graphical models are powerful tools for modeling collaborative filtering, but careful design is necessary to achieve good performance.},
journal = {Inf. Retr.},
month = jun,
pages = {357–382},
numpages = {26},
keywords = {Probabilistic model, Graphical model, Collaborative filtering}
}

@article{10.1007/s10791-006-4536-3,
author = {Nyongesa, H. O. and Maleki-Dizaji, S.},
title = {User Modelling Using Evolutionary Interactive Reinforcement Learning},
year = {2006},
issue_date = {June      2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-4536-3},
doi = {10.1007/s10791-006-4536-3},
abstract = {As the volume and variety of information sources continues to grow, there is increasing difficulty with respect to obtaining information that accurately matches user information needs. A number of factors affect information retrieval effectiveness (the accuracy of matching user information needs against the retrieved information). First, users often do not present search queries in the form that optimally represents their information need. Second, the measure of a document's relevance is often highly subjective between different users. Third, information sources might contain heterogeneous documents, in multiple formats and the representation of documents is not unified. This paper discusses an approach for improvement of information retrieval effectiveness from document databases. It is proposed that retrieval effectiveness can be improved by applying computational intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative (subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An information retrieval is developed whose retrieval effectiveness is evaluated using traditional precision and recall.},
journal = {Inf. Retr.},
month = jun,
pages = {343–355},
numpages = {13},
keywords = {Interactive evolutionary learning, Information relevance, User information needs modelling, Adaptive information retrieval}
}

@article{10.1007/s10791-006-3609-7,
author = {Fragos, Kostas and Maistros, Yannis},
title = {A Goodness of Fit Test Approach in Information Retrieval},
year = {2006},
issue_date = {June      2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-3609-7},
doi = {10.1007/s10791-006-3609-7},
abstract = {In many probabilistic modeling approaches to Information Retrieval we are interested in estimating how well a document model "fits" the user's information need (query model). On the other hand in statistics, goodness of fit tests are well established techniques for assessing the assumptions about the underlying distribution of a data set. Supposing that the query terms are randomly distributed in the various documents of the collection, we actually want to know whether the occurrences of the query terms are more frequently distributed by chance in a particular document. This can be quantified by the so-called goodness of fit tests. In this paper, we present a new document ranking technique based on Chi-square goodness of fit tests. Given the null hypothesis that there is no association between the query terms  q  and the document  d  irrespective of any chance occurrences, we perform a Chi-square goodness of fit test for assessing this hypothesis and calculate the corresponding Chi-square values. Our retrieval formula is based on ranking the documents in the collection according to these calculated Chi-square values. The method was evaluated over the entire test collection of TREC data, on disks 4 and 5, using the topics of TREC-7 and TREC-8 (50 topics each) conferences. It performs well, outperforming steadily the classical OKAPI term frequency weighting formula but below that of KL-Divergence from language modeling approach. Despite this, we believe that the technique is an important non-parametric way of thinking of retrieval, offering the possibility to try simple alternative retrieval formulas within  goodness-of-fit  statistical tests' framework, modeling the data in various ways estimating or assigning any arbitrary theoretical distribution in terms.},
journal = {Inf. Retr.},
month = jun,
pages = {331–342},
numpages = {12},
keywords = {Information Retrieval, goodness of fit tests}
}

@article{10.1007/s10791-006-1682-6,
author = {Cummins, Ronan and O'Riordan, Colm},
title = {Evolving Local and Global Weighting Schemes in Information Retrieval},
year = {2006},
issue_date = {June      2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-1682-6},
doi = {10.1007/s10791-006-1682-6},
abstract = {This paper describes a method, using Genetic Programming, to automatically determine term weighting schemes for the vector space model. Based on a set of queries and their human determined relevant documents, weighting schemes are evolved which achieve a high average precision. In Information Retrieval (IR) systems, useful information for term weighting schemes is available from the query, individual documents and the collection as a whole.We evolve term weighting schemes in both local (within-document) and global (collection-wide) domains which interact with each other correctly to achieve a high average precision. These weighting schemes are tested on well-known test collections and are compared to the traditional  tf-idf  weighting scheme and to the BM25 weighting scheme using standard IR performance metrics.Furthermore, we show that the global weighting schemes evolved on small collections also increase average precision on larger TREC data. These global weighting schemes are shown to adhere to Luhn's resolving power as both high and low frequency terms are assigned low weights. However, the local weightings evolved on small collections do not perform as well on large collections. We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on large collections.},
journal = {Inf. Retr.},
month = jun,
pages = {311–330},
numpages = {20},
keywords = {Genetic Programming, Term-Weighting Schemes, Information Retrieval}
}

@article{10.1007/s10791-006-1541-5,
author = {Lind\'{e}n, Krister},
title = {Multilingual Modeling of Cross-Lingual Spelling Variants},
year = {2006},
issue_date = {June      2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-1541-5},
doi = {10.1007/s10791-006-1541-5},
abstract = {Technical term translations are important for cross-lingual information retrieval. In many languages, new technical terms have a common origin rendered with different spelling of the underlying sounds, also known as cross-lingual spelling variants (CLSV).To find the best CLSV in a text database index, we contribute a formulation of the problem in a probabilistic framework, and implement this with an instance of the general edit distance using weighted finite-state transducers. Some training data is required when estimating the costs for the general edit distance. We demonstrate that after some basic training our new multilingual model is robust and requires little or no adaptation for covering additional languages, as the model takes advantage of language independent transliteration patterns.We train the model with medical terms in seven languages and test it with terms from varied domains in six languages. Two test languages are not in the training data. Against a large text database index, we achieve 64---78 % precision at the point of 100% recall. This is a relative improvement of 22% on the simple edit distance.},
journal = {Inf. Retr.},
month = jun,
pages = {295–310},
numpages = {16},
keywords = {Term translations, Cross-lingual information retrieval, Systematic spelling variants, General edit distance}
}

@article{10.1007/s10791-006-0884-2,
author = {Airio, Eija},
title = {Word Normalization and Decompounding in Mono- and Bilingual IR},
year = {2006},
issue_date = {June      2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-006-0884-2},
doi = {10.1007/s10791-006-0884-2},
abstract = {The present research studies the impact of decompounding and two different word normalization methods, stemming and lemmatization, on monolingual and bilingual retrieval. The languages in the monolingual runs are English, Finnish, German and Swedish. The source language of the bilingual runs is English, and the target languages are Finnish, German and Swedish. In the monolingual runs, retrieval in a lemmatized compound index gives almost as good results as retrieval in a decompounded index, but in the bilingual runs differences are found: retrieval in a lemmatized decompounded index performs better than retrieval in a lemmatized compound index. The reason for the poorer performance of indexes without decompounding in bilingual retrieval is the difference between the source language and target languages: phrases are used in English, while compounds are used instead of phrases in Finnish, German and Swedish. No remarkable performance differences could be found between stemming and lemmatization.},
journal = {Inf. Retr.},
month = jun,
pages = {249–271},
numpages = {23},
keywords = {Monolingual information retrieval, stemming, lemmatization, decompounding, bilingual information retrieval}
}

@article{10.1007/s10791-006-0882-4,
author = {Madigan, David and Vardi, Yehuda and Weissman, Ishay},
title = {Extreme Value Theory Applied to Document Retrieval from Large Collections},
year = {2006},
issue_date = {June      2006},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {9},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10

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repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr