text:
2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9148-2},
doi = {10.1007/s10791-010-9148-2},
journal = {Inf. Retr.},
month = dec,
pages = {693–694},
numpages = {2}
}

@article{10.1007/s10791-010-9140-x,
author = {Diaz, Fernando},
title = {A Generative Theory of Relevance},
year = {2010},
issue_date = {December  2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9140-x},
doi = {10.1007/s10791-010-9140-x},
journal = {Inf. Retr.},
month = dec,
pages = {689–692},
numpages = {4}
}

@article{10.1007/s10791-010-9134-8,
author = {He, Yin and Liu, Tie-Yan},
title = {Tendency Correlation Analysis for Direct Optimization of Evaluation Measures in Information Retrieval},
year = {2010},
issue_date = {December  2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9134-8},
doi = {10.1007/s10791-010-9134-8},
abstract = {Direct optimization of evaluation measures has become an important branch of learning to rank for information retrieval (IR). Since IR evaluation measures are difficult to optimize due to their non-continuity and non-differentiability, most direct optimization methods optimize some surrogate functions instead, which we call surrogate measures. A critical issue regarding these methods is whether the optimization of the surrogate measures can really lead to the optimization of the original IR evaluation measures. In this work, we perform formal analysis on this issue. We propose a concept named "tendency correlation" to describe the relationship between a surrogate measure and its corresponding IR evaluation measure. We show that when a surrogate measure has arbitrarily strong tendency correlation with an IR evaluation measure, the optimization of it will lead to the effective optimization of the original IR evaluation measure. Then, we analyze the tendency correlations of the surrogate measures optimized in a number of direct optimization methods. We prove that the surrogate measures in SoftRank and ApproxRank can have arbitrarily strong tendency correlation with the original IR evaluation measures, regardless of the data distribution, when some parameters are appropriately set. However, the surrogate measures in SVM              MAP             , DORM              NDCG             , PermuRank              MAP             , and SVM              NDCG              cannot have arbitrarily strong tendency correlation with the original IR evaluation measures on certain distributions of data. Therefore SoftRank and ApproxRank are theoretically sounder than SVM              MAP             , DORM              NDCG             , PermuRank              MAP             , and SVM              NDCG             , and are expected to result in better ranking performances. Our theoretical findings can explain the experimental results observed on public benchmark datasets.},
journal = {Inf. Retr.},
month = dec,
pages = {657–688},
numpages = {32},
keywords = {Direct optimization, Information retrieval measures, Learning to rank, Tendency correlation}
}

@article{10.1007/s10791-010-9132-x,
author = {Penev, Alex and Wong, Raymond K.},
title = {Structure vs. Content in Hierarchical Corpora},
year = {2010},
issue_date = {December  2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9132-x},
doi = {10.1007/s10791-010-9132-x},
abstract = {We propose and describe  Parameterized filesystem HITS  (PFH), a lightweight rearrangement algorithm that performs a re-ranking of existing content-only search results in tree-like hierarchical corpora, such as a filesystem. PFH does this by combining the content analysis of the results with structural analysis of how they are organized. An parameter is used to vary the structure-content bias, allowing us to observe changes in performance when we place more emphasis on structure or on content. Using real and simulated data, experiments are provided to show that = 0.8 (i.e, 20% structure and 80% content) can substantially boost Mean Reciprocal Rank while keeping other IR metrics steady. Such an algorithm may be useful for building retrieval systems over hierarchical content such as Desktop Search, technical documents and books.},
journal = {Inf. Retr.},
month = dec,
pages = {636–656},
numpages = {21},
keywords = {Desktop search, HITS, Information retrieval, Ranking}
}

@article{10.1007/s10791-010-9127-7,
author = {Fang, Yi and Si, Luo and Mathur, Aditya P.},
title = {Discriminative Graphical Models for Faculty Homepage Discovery},
year = {2010},
issue_date = {December  2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9127-7},
doi = {10.1007/s10791-010-9127-7},
abstract = {Faculty homepage discovery is an important step toward building an academic portal. Although the general homepage finding tasks have been well studied (e.g., TREC-2001 Web Track), faculty homepage discovery has its own special characteristics and not much focused research has been conducted for this task. In this paper, we view faculty homepage discovery as text categorization problems by utilizing Yahoo BOSS API to generate a small list of high-quality candidate homepages. Because the labels of these pages are not independent, standard text categorization methods such as logistic regression, which classify each page separately, are not well suited for this task. By defining homepage dependence graph, we propose a conditional undirected graphical model to make joint predictions by capturing the dependence of the decisions on all the candidate pages. Three cases of dependencies among faculty candidate homepages are considered for constructing the graphical model. Our model utilizes a discriminative approach so that any informative features can be used conveniently. Learning and inference can be done relatively efficiently for the joint prediction model because the homepage dependence graphs resulting from the three cases of dependencies are not densely connected. An extensive set of experiments have been conducted on two testbeds to show the effectiveness of the proposed discriminative graphical model.},
journal = {Inf. Retr.},
month = dec,
pages = {618–635},
numpages = {18},
keywords = {Information retrieval, Homepage finding, Discriminative graphical models}
}

@article{10.1007/s10791-010-9126-8,
author = {Smith, Larry H. and Wilbur, W. John},
title = {Finding Related Sentence Pairs in MEDLINE},
year = {2010},
issue_date = {December  2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9126-8},
doi = {10.1007/s10791-010-9126-8},
abstract = {We explore the feasibility of automatically identifying sentences in different MEDLINE abstracts that are related in meaning. We compared traditional vector space models with machine learning methods for detecting relatedness, and found that machine learning was superior. The Huber method, a variant of Support Vector Machines which minimizes the modified Huber loss function, achieves 73% precision when the score cutoff is set high enough to identify about one related sentence per abstract on average. We illustrate how an abstract viewed in PubMed might be modified to present the related sentences found in other abstracts by this automatic procedure.},
journal = {Inf. Retr.},
month = dec,
pages = {601–617},
numpages = {17},
keywords = {Related sentences, Machine learning}
}

@article{10.1007/s10791-010-9137-5,
author = {Trotman, Andrew and Geva, Shlomo and Kamps, Jaap and Lalmas, Mounia and Murdock, Vanessa},
title = {Current Research in Focused Retrieval and Result Aggregation},
year = {2010},
issue_date = {October   2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9137-5},
doi = {10.1007/s10791-010-9137-5},
abstract = {Both focused retrieval and result aggregation provide the user with answers to their information needs, rather than just pointers to whole documents. Focused retrieval identifies not only relevant documents but also which parts of those documents are relevant, thus reducing the time it takes the user to navigate in a document. Result aggregation is used when the best way to fulfil the user's need is to draw from many different information sources (different collections, documents, or parts of the same document), and to aggregate these into a single result, thus reducing the time it takes the user to fulfil their information need. This special issue includes seven papers showing the breadth and depth of the current state of research in these two branches of information retrieval. They include descriptions of live result aggregation systems, and experimental focussed retrieval systems including sentence retrieval, question answering, and entity ranking; as well as metrics for passage retrieval. To introduce this special issue, we provide an overview of the work presented in these papers.},
journal = {Inf. Retr.},
month = oct,
pages = {407–411},
numpages = {5},
keywords = {Users, Metrics, Focused retrieval, Result aggregation}
}

@article{10.1007/s10791-010-9135-7,
author = {Demartini, Gianluca and Firan, Claudiu S. and Iofciu, Tereza and Krestel, Ralf and Nejdl, Wolfgang},
title = {Why Finding Entities in Wikipedia is Difficult, Sometimes},
year = {2010},
issue_date = {October   2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9135-7},
doi = {10.1007/s10791-010-9135-7},
abstract = {Entity Retrieval (ER)--in comparison to classical search--aims at finding individual entities instead of relevant documents. Finding a list of entities requires therefore techniques different to classical search engines. In this paper, we present a model to describe entities more formally and how an ER system can be build on top of it. We compare different approaches designed for finding entities in Wikipedia and report on results using standard test collections. An analysis of entity-centric queries reveals different aspects and problems related to ER and shows limitations of current systems performing ER with Wikipedia. It also indicates which approaches are suitable for which kinds of queries.},
journal = {Inf. Retr.},
month = oct,
pages = {534–567},
numpages = {34},
keywords = {Model, Algorithms, Entity search, Evaluation, Experimentation}
}

@article{10.1007/s10791-010-9133-9,
author = {Arvola, Paavo and Kek\"{a}l\"{a}inen, Jaana and Junkkari, Marko},
title = {Expected Reading Effort in Focused Retrieval Evaluation},
year = {2010},
issue_date = {October   2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9133-9},
doi = {10.1007/s10791-010-9133-9},
abstract = {This study introduces a novel framework for evaluating passage and XML retrieval. The framework focuses on a user's effort to localize relevant content in a result document. Measuring the effort is based on a system guided reading order of documents. The effort is calculated as the quantity of text the user is expected to browse through. More specifically, this study seeks evaluation metrics for retrieval methods following a specific fetch and browse approach, where in the fetch phase documents are ranked in decreasing order according to their document score, like in document retrieval. In the browse phase, for each retrieved document, a set of non-overlapping passages representing the relevant text within the document is retrieved. In other words, the passages of the document are re-organized, so that the best matching passages are read first in sequential order. We introduce an application scenario motivating the framework, and propose sample metrics based on the framework. These metrics give a basis for the comparison of effectiveness between traditional document retrieval and passage/XML retrieval and illuminate the benefit of passage/XML retrieval.},
journal = {Inf. Retr.},
month = oct,
pages = {460–484},
numpages = {25},
keywords = {Metrics, Evaluation, Small screen devices, Passage retrieval, XML retrieval}
}

@article{10.1007/s10791-010-9131-y,
author = {Moriceau, V\'{e}ronique and Tannier, Xavier},
title = {FIDJI: Using Syntax for Validating Answers in Multiple Documents},
year = {2010},
issue_date = {October   2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9131-y},
doi = {10.1007/s10791-010-9131-y},
abstract = {This article presents FIDJI, a question-answering (QA) system for French. FIDJI combines syntactic information with traditional QA techniques such as named entity recognition and term weighting; it does not require any pre-processing other than classical search engine indexing. Among other uses of syntax, we experiment in this system the validation of answers through different documents, as well as specific techniques for answering different types of questions (e.g., yes/no or list questions). We present several experiments which show the benefits of syntactic analysis, as well as multi-document validation. Different types of questions and corpora are tested, and specificities are commented. Links with result aggregation are also discussed.},
journal = {Inf. Retr.},
month = oct,
pages = {507–533},
numpages = {27},
keywords = {Syntactic analysis, Result aggregation, Question answering, Multi-document validation, Focused retrieval}
}

@article{10.1007/s10791-010-9130-z,
author = {Kaptein, Rianne and Marx, Maarten},
title = {Focused Retrieval and Result Aggregation with Political Data},
year = {2010},
issue_date = {October   2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9130-z},
doi = {10.1007/s10791-010-9130-z},
abstract = {This paper presents a case-study in which we use a large semi-structured data set consisting of official transcripts of meetings of the Dutch parliament for focused retrieval and result aggregation. Transcripts of meetings are a document genre characterized by a complex narrative structure. The essence is not only what is said, but also by who and to whom. We have notes of more than 40 years of Dutch parliamentary debates where this structure is exploited to automatically make semantic annotations. These annotations yield numerous new ways of searching, browsing, mining and summarizing these documents. Concerning result aggregation, we summarise and visualise the structure of meetings into tables of content and interruption graphs. The contents of meetings or parts of meetings are condensed into word clouds that are created using a parsimonious language model. Furthermore, we have developed a search engine that exploits the structure and annotations of our data making it possible to provide entry points, to group search results, and to use faceted search techniques for data-exploration. Evaluation shows that our content and structure summarization tools provide a good first impression of a debate. Users reported that, compared to a standard document retrieval system, our search engine gives a better overview of the data. Search tasks are performed faster and the users felt more certain of their answers.},
journal = {Inf. Retr.},
month = oct,
pages = {412–433},
numpages = {22},
keywords = {Result aggregation, Political data, Focused retrieval, Word clouds}
}

@article{10.1007/s10791-009-9125-9,
author = {Pehcevski, Jovan and Thom, James A. and Vercoustre, Anne-Marie and Naumovski, Vladimir},
title = {Entity Ranking in Wikipedia: Utilising Categories, Links and Topic Difficulty Prediction},
year = {2010},
issue_date = {October   2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9125-9},
doi = {10.1007/s10791-009-9125-9},
abstract = {Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag names of entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we describe a system we developed for ranking Wikipedia entities in answer to a query. The entity ranking approach implemented in our system utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the entity examples (when provided) to retrieve relevant entities as answers to the query. We also extend our entity ranking approach by utilising the knowledge of predicted classes of topic difficulty. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments demonstrate that the use of categories and the link structure of Wikipedia can significantly improve entity ranking effectiveness, and that topic difficulty prediction is a promising approach that could also be exploited to further improve the entity ranking performance.},
journal = {Inf. Retr.},
month = oct,
pages = {568–600},
numpages = {33},
keywords = {Wikipedia, XML Retrieval, Entity ranking, INEX}
}

@article{10.1007/s10791-009-9122-z,
author = {Losada, David E.},
title = {Statistical Query Expansion for Sentence Retrieval and Its Effects on Weak and Strong Queries},
year = {2010},
issue_date = {October   2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9122-z},
doi = {10.1007/s10791-009-9122-z},
abstract = {The retrieval of sentences that are relevant to a given information need is a challenging passage retrieval task. In this context, the well-known vocabulary mismatch problem arises severely because of the fine granularity of the task. Short queries, which are usually the rule rather than the exception, aggravate the problem. Consequently, effective sentence retrieval methods tend to apply some form of query expansion, usually based on pseudo-relevance feedback. Nevertheless, there are no extensive studies comparing different statistical expansion strategies for sentence retrieval. In this work we study thoroughly the effect of distinct statistical expansion methods on sentence retrieval. We start from a set of retrieved documents in which relevant sentences have to be found. In our experiments different term selection strategies are evaluated and we provide empirical evidence to show that expansion before sentence retrieval yields competitive performance. This is particularly novel because expansion for sentence retrieval is often done after sentence retrieval (i.e. expansion terms are mined from a ranked set of sentences) and there are no comparative results available between both types of expansion. Furthermore, this comparison is particularly valuable because there are important implications in time efficiency. We also carefully analyze expansion on weak and strong queries and demonstrate clearly that expanding queries before sentence retrieval is not only more convenient for efficiency purposes, but also more effective when handling poor queries.},
journal = {Inf. Retr.},
month = oct,
pages = {485–506},
numpages = {22},
keywords = {Sentence retrieval, Information retrieval, Query expansion}
}

@article{10.1007/s10791-009-9121-0,
author = {Paris, C\'{e}cile and Wan, Stephen and Thomas, Paul},
title = {Focused and Aggregated Search: A Perspective from Natural Language Generation},
year = {2010},
issue_date = {October   2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9121-0},
doi = {10.1007/s10791-009-9121-0},
abstract = {Users are often faced with complex information needs that are not easily represented as a single query. With current technology, the burden of issuing these individual queries, analysing retrieved documents for relevance, as well as aggregating results falls upon the time-poor and informationally overloaded user. Aggregated search techniques represent the new generation of search applications that endeavour to help users perform these complex tasks. However, the way in which different data types are combined in current aggregated search applications is often performed using static hard-coded structures. We suggest that a useful alternative is to marry techniques from natural language generation, such as text planning and summarisation, in order to dynamically determine the best organisation of retrieved information. These organisations can be motivated by linguistic theories that consider issues such as the role that the information plays to facilitate a task, and the relationships between different pieces of information. With reference to a discourse strategy, it is possible to draw on several data sources automatically to generate a useful, focused, and coherent answer. We focus on exploring the parallels between aggregated search and natural language generation in the hope that the fields can be mutually informed, leading to further advances in the way search technologies can better serve the user. These issues are discussed and presented with examples of existing systems across different domains.},
journal = {Inf. Retr.},
month = oct,
pages = {434–459},
numpages = {26},
keywords = {Orchestration, Discourse strategy, Linguistics}
}

@article{10.1007/s10791-010-9129-5,
author = {Vallet, David},
title = {John Davies, Marko Grobelnik and Dunja Mladenic: Semantic Knowledge Management},
year = {2010},
issue_date = {August    2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9129-5},
doi = {10.1007/s10791-010-9129-5},
journal = {Inf. Retr.},
month = aug,
pages = {403–406},
numpages = {4}
}

@article{10.1007/s10791-010-9128-6,
author = {Foley, Colum},
title = {David Gibbon, Zhu Liu: Introduction to Video Search Engines},
year = {2010},
issue_date = {August    2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-010-9128-6},
doi = {10.1007/s10791-010-9128-6},
journal = {Inf. Retr.},
month = aug,
pages = {398–402},
numpages = {5}
}

@article{10.1007/s10791-009-9124-x,
author = {Qin, Tao and Liu, Tie-Yan and Li, Hang},
title = {A General Approximation Framework for Direct Optimization of Information Retrieval Measures},
year = {2010},
issue_date = {August    2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9124-x},
doi = {10.1007/s10791-009-9124-x},
abstract = {Recently direct optimization of information retrieval (IR) measures has become a new trend in learning to rank. In this paper, we propose a general framework for direct optimization of IR measures, which enjoys several theoretical advantages. The general framework, which can be used to optimize most IR measures, addresses the task by approximating the IR measures and optimizing the approximated surrogate functions. Theoretical analysis shows that a high approximation accuracy can be achieved by the framework. We take average precision (AP) and normalized discounted cumulated gains (NDCG) as examples to demonstrate how to realize the proposed framework. Experiments on benchmark datasets show that the algorithms deduced from our framework are very effective when compared to existing methods. The empirical results also agree well with the theoretical results obtained in the paper.},
journal = {Inf. Retr.},
month = aug,
pages = {375–397},
numpages = {23},
keywords = {Position function approximation, Accuracy analysis, Truncation function approximation, Direct optimization of IR measures, Learning to rank}
}

@article{10.1007/s10791-009-9123-y,
author = {Qin, Tao and Liu, Tie-Yan and Xu, Jun and Li, Hang},
title = {LETOR: A Benchmark Collection for Research on Learning to Rank for Information Retrieval},
year = {2010},
issue_date = {August    2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9123-y},
doi = {10.1007/s10791-009-9123-y},
abstract = {LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the LETOR collection and show how it can be used in different kinds of researches. Specifically, we describe how the document corpora and query sets in LETOR are selected, how the documents are sampled, how the learning features and meta information are extracted, and how the datasets are partitioned for comprehensive evaluation. We then compare several state-of-the-art learning to rank algorithms on LETOR, report their ranking performances, and make discussions on the results. After that, we discuss possible new research topics that can be supported by LETOR, in addition to algorithm comparison. We hope that this paper can help people to gain deeper understanding of LETOR, and enable more interesting research projects on learning to rank and related topics.},
journal = {Inf. Retr.},
month = aug,
pages = {346–374},
numpages = {29},
keywords = {Feature extraction, Benchmark datasets, Information retrieval, Learning to rank}
}

@article{10.1007/s10791-009-9119-7,
author = {Couto, T. and Ziviani, N. and Calado, P. and Cristo, M. and Gon\c{c}alves, M. and Moura, E. S. and Brand\~{a}o, W.},
title = {Classifying Documents with Link-Based Bibliometric Measures},
year = {2010},
issue_date = {August    2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9119-7},
doi = {10.1007/s10791-009-9119-7},
abstract = {Automatic document classification can be used to organize documents in a digital library, construct on-line directories, improve the precision of web searching, or help the interactions between user and search engines. In this paper we explore how linkage information inherent to different document collections can be used to enhance the effectiveness of classification algorithms. We have experimented with three link-based bibliometric measures, co-citation, bibliographic coupling and Amsler, on three different document collections: a digital library of computer science papers, a web directory and an on-line encyclopedia. Results show that both hyperlink and citation information can be used to learn reliable and effective classifiers based on a kNN classifier. In one of the test collections used, we obtained improvements of up to 69.8% of macro-averaged F
                        1 over the traditional text-based kNN classifier, considered as the baseline measure in our experiments. We also present alternative ways of combining bibliometric based classifiers with text based classifiers. Finally, we conducted studies to analyze the situation in which the bibliometric-based classifiers failed and show that in such cases it is hard to reach consensus regarding the correct classes, even for human judges.},
journal = {Inf. Retr.},
month = aug,
pages = {315–345},
numpages = {31},
keywords = {Web directories, Digital libraries, Text classification, Links}
}

@article{10.1007/s10791-009-9120-1,
author = {Liu, Tie-Yan and Joachims, Thorsten and Li, Hang and Zhai, Chengxiang},
title = {Introduction to Special Issue on Learning to Rank for Information Retrieval},
year = {2010},
issue_date = {June      2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9120-1},
doi = {10.1007/s10791-009-9120-1},
journal = {Inf. Retr.},
month = jun,
pages = {197–200},
numpages = {4}
}

@article{10.1007/s10791-009-9117-9,
author = {Bai, Bing and Weston, Jason and Grangier, David and Collobert, Ronan and Sadamasa, Kunihiko and Qi, Yanjun and Chapelle, Olivier and Weinberger, Kilian},
title = {Learning to Rank with (a Lot of) Word Features},
year = {2010},
issue_date = {June      2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9117-9},
doi = {10.1007/s10791-009-9117-9},
abstract = {In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as cross-language retrieval or online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, correlated feature hashing and sparsification. We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.},
journal = {Inf. Retr.},
month = jun,
pages = {291–314},
numpages = {24},
keywords = {Content matching, Learning to rank, Feature hashing, Cross language retrieval, Semantic indexing}
}

@article{10.1007/s10791-009-9116-x,
author = {Yilmaz, Emine and Robertson, Stephen},
title = {On the Choice of Effectiveness Measures for Learning to Rank},
year = {2010},
issue_date = {June      2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9116-x},
doi = {10.1007/s10791-009-9116-x},
abstract = {Most current machine learning methods for building search engines are based on the assumption that there is a target evaluation metric that evaluates the quality of the search engine with respect to an end user and the engine should be trained to optimize for that metric. Treating the target evaluation metric as a given, many different approaches (e.g. LambdaRank, SoftRank, RankingSVM, etc.) have been proposed to develop methods for optimizing for retrieval metrics. Target metrics used in optimization act as bottlenecks that summarize the training data and it is known that some evaluation metrics are more informative than others. In this paper, we consider the effect of the target evaluation metric on learning to rank. In particular, we question the current assumption that retrieval systems should be designed to directly optimize for a metric that is assumed to evaluate user satisfaction. We show that even if user satisfaction can be measured by a metric X, optimizing the engine on a training set for a more informative metric Y may result in a better test performance according to X (as compared to optimizing the engine directly for X on the training set). We analyze the situations as to when there is a significant difference in the two cases in terms of the amount of available training data and the number of dimensions of the feature space.},
journal = {Inf. Retr.},
month = jun,
pages = {271–290},
numpages = {20},
keywords = {Empirical risk minimization, Evaluation, Evaluation metrics, Learning to rank, Training}
}

@article{10.1007/s10791-009-9112-1,
author = {Wu, Qiang and Burges, Christopher J. and Svore, Krysta M. and Gao, Jianfeng},
title = {Adapting Boosting for Information Retrieval Measures},
year = {2010},
issue_date = {June      2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9112-1},
doi = {10.1007/s10791-009-9112-1},
abstract = {We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, which has been shown to be empirically optimal for a widely used information retrieval measure. Our algorithm is based on boosted regression trees, although the ideas apply to any weak learners, and it is significantly faster in both train and test phases than the state of the art, for comparable accuracy. We also show how to find the optimal linear combination for any two rankers, and we use this method to solve the line search problem exactly during boosting. In addition, we show that starting with a previously trained model, and boosting using its residuals, furnishes an effective technique for model adaptation, and we give significantly improved results for a particularly pressing problem in web search--training rankers for markets for which only small amounts of labeled data are available, given a ranker trained on much more data from a larger market.},
journal = {Inf. Retr.},
month = jun,
pages = {254–270},
numpages = {17},
keywords = {Learning to rank, Boosting, Web search}
}

@article{10.1007/s10791-009-9111-2,
author = {Chen, Depin and Xiong, Yan and Yan, Jun and Xue, Gui-Rong and Wang, Gang and Chen, Zheng},
title = {Knowledge Transfer for Cross Domain Learning to Rank},
year = {2010},
issue_date = {June      2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9111-2},
doi = {10.1007/s10791-009-9111-2},
abstract = {Recently, learning to rank technology is attracting increasing attention from both academia and industry in the areas of machine learning and information retrieval. A number of algorithms have been proposed to rank documents according to the user-given query using a human-labeled training dataset. A basic assumption behind general learning to rank algorithms is that the training and test data are drawn from the same data distribution. However, this assumption does not always hold true in real world applications. For example, it can be violated when the labeled training data become outdated or originally come from another domain different from its counterpart of test data. Such situations bring a new problem, which we define as cross domain learning to rank. In this paper, we aim at improving the learning of a ranking model in target domain by leveraging knowledge from the outdated or out-of-domain data (both are referred to as source domain data). We first give a formal definition of the cross domain learning to rank problem. Following this, two novel methods are proposed to conduct knowledge transfer at feature level and instance level, respectively. These two methods both utilize Ranking SVM as the basic learner. In the experiments, we evaluate these two methods using data from benchmark datasets for document retrieval. The results show that the feature-level transfer method performs better with steady improvements over baseline approaches across different datasets, while the instance-level transfer method comes out with varying performance depending on the dataset used.},
journal = {Inf. Retr.},
month = jun,
pages = {236–253},
numpages = {18},
keywords = {Knowledge transfer, Information retrieval, Ranking SVM, Learning to rank}
}

@article{10.1007/s10791-009-9110-3,
author = {Chapelle, Olivier and Wu, Mingrui},
title = {Gradient Descent Optimization of Smoothed Information Retrieval Metrics},
year = {2010},
issue_date = {June      2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9110-3},
doi = {10.1007/s10791-009-9110-3},
abstract = {Most ranking algorithms are based on the optimization of some loss functions, such as the pairwise loss. However, these loss functions are often different from the criteria that are adopted to measure the quality of the web page ranking results. To overcome this problem, we propose an algorithm which aims at directly optimizing popular measures such as the Normalized Discounted Cumulative Gain and the Average Precision. The basic idea is to minimize a smooth approximation of these measures with gradient descent. Crucial to this kind of approach is the choice of the smoothing factor. We provide various theoretical analysis on that choice and propose an annealing algorithm to iteratively minimize a less and less smoothed approximation of the measure of interest. Results on the Letor benchmark datasets show that the proposed algorithm achieves state-of-the-art performances.},
journal = {Inf. Retr.},
month = jun,
pages = {216–235},
numpages = {20},
keywords = {Learning to rank, Annealing, Gradient descent}
}

@article{10.1007/s10791-009-9109-9,
author = {Chapelle, O. and Keerthi, S. S.},
title = {Efficient Algorithms for Ranking with SVMs},
year = {2010},
issue_date = {June      2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9109-9},
doi = {10.1007/s10791-009-9109-9},
abstract = {RankSVM (Herbrich et al. in Advances in large margin classifiers. MIT Press, Cambridge, MA, 2000; Joachims in Proceedings of the ACM conference on knowledge discovery and data mining (KDD), 2002) is a pairwise method for designing ranking models. SVMLight is the only publicly available software for RankSVM. It is slow and, due to incomplete training with it, previous evaluations show RankSVM to have inferior ranking performance. We propose new methods based on primal Newton method to speed up RankSVM training and show that they are 5 orders of magnitude faster than SVMLight. Evaluation on the Letor benchmark datasets after complete training using such methods shows that the performance of RankSVM is excellent.},
journal = {Inf. Retr.},
month = jun,
pages = {201–215},
numpages = {15},
keywords = {AUC optimization, Support vector machines, Ranking}
}

@article{10.1007/s10791-009-9118-8,
author = {Bendersky, Michael and Kurland, Oren},
title = {Utilizing Passage-Based Language Models for Ad Hoc Document Retrieval},
year = {2010},
issue_date = {April     2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9118-8},
doi = {10.1007/s10791-009-9118-8},
abstract = {To cope with the fact that, in the ad hoc retrieval setting, documents relevant to a query could contain very few (short) parts (passages) with query-related information, researchers proposed passage-based document ranking approaches. We show that several of these retrieval methods can be understood, and new ones can be derived, using the same probabilistic model. We use language-model estimates to instantiate specific retrieval algorithms, and in doing so present a novel passage language model that integrates information from the containing document to an extent controlled by the estimated document homogeneity. Several document-homogeneity measures that we present yield passage language models that are more effective than the standard passage model for basic document retrieval and for constructing and utilizing passage-based relevance models; these relevance models also outperform a document-based relevance model. Finally, we demonstrate the merits in using the document-homogeneity measures for integrating document-query and passage-query similarity information for document retrieval.},
journal = {Inf. Retr.},
month = apr,
pages = {157–187},
numpages = {31},
keywords = {Ad hoc document retrieval, Passage-based relevance models, Document homogeneity, Relevance models, Passage-based language models}
}

@article{10.1007/s10791-009-9115-y,
author = {Mogotsi, I. C.},
title = {Christopher D. Manning, Prabhakar Raghavan, and Hinrich Sch\"{u}Tze: Introduction to Information Retrieval},
year = {2010},
issue_date = {April     2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9115-y},
doi = {10.1007/s10791-009-9115-y},
journal = {Inf. Retr.},
month = apr,
pages = {192–195},
numpages = {4}
}

@article{10.1007/s10791-009-9114-z,
author = {Buscher, Georg},
title = {Bernard J. Jansen, Amanda Spink, Isak Taksa: Handbook of Research on Web Log Analysis},
year = {2010},
issue_date = {April     2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9114-z},
doi = {10.1007/s10791-009-9114-z},
journal = {Inf. Retr.},
month = apr,
pages = {188–191},
numpages = {4}
}

@article{10.1007/s10791-009-9113-0,
author = {Naughton, M. and Stokes, N. and Carthy, J.},
title = {Sentence-Level Event Classification in Unstructured Texts},
year = {2010},
issue_date = {April     2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9113-0},
doi = {10.1007/s10791-009-9113-0},
abstract = {The ability to correctly classify sentences that describe events is an important task for many natural language applications such as Question Answering (QA) and Text Summarisation. In this paper, we treat event detection as a sentence level text classification problem. Overall, we compare the performance of discriminative versus generative approaches to this task: namely, a Support Vector Machine (SVM) classifier versus a Language Modeling (LM) approach. We also investigate a rule-based method that uses handcrafted lists of `trigger' terms derived from WordNet. Two datasets are used in our experiments to test each approach on six different event types, i.e., Die, Attack, Injure, Meet, Transport and Charge-Indict. Our experimental results show that the trained SVM classifier significantly outperforms the simple rule-based system and language modeling approach on both datasets: ACE (F1 66% vs. 45% and 38%, respectively) and IBC (F1 92% vs. 88% and 74%, respectively). A detailed error analysis framework for the task is also provided which separates errors into different types: semantic, inference, continuous and trigger-less.},
journal = {Inf. Retr.},
month = apr,
pages = {132–156},
numpages = {25},
keywords = {Machine learning, Language modeling, Information extraction, Event detection}
}

@article{10.1007/s10791-009-9108-x,
author = {Aljaber, Bader and Stokes, Nicola and Bailey, James and Pei, Jian},
title = {Document Clustering of Scientific Texts Using Citation Contexts},
year = {2010},
issue_date = {April     2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9108-x},
doi = {10.1007/s10791-009-9108-x},
abstract = {Document clustering has many important applications in the area of data mining and information retrieval. Many existing document clustering techniques use the "bag-of-words" model to represent the content of a document. However, this representation is only effective for grouping related documents when these documents share a large proportion of lexically equivalent terms. In other words, instances of synonymy between related documents are ignored, which can reduce the effectiveness of applications using a standard full-text document representation. To address this problem, we present a new approach for clustering scientific documents, based on the utilization of citation contexts. A citation context is essentially the text surrounding the reference markers used to refer to other scientific works. We hypothesize that citation contexts will provide relevant synonymous and related vocabulary which will help increase the effectiveness of the bag-of-words representation. In this paper, we investigate the power of these citation-specific word features, and compare them with the original document's textual representation in a document clustering task on two collections of labeled scientific journal papers from two distinct domains: High Energy Physics and Genomics. We also compare these text-based clustering techniques with a link-based clustering algorithm which determines the similarity between documents based on the number of co-citations, that is in-links represented by citing documents and out-links represented by cited documents. Our experimental results indicate that the use of citation contexts, when combined with the vocabulary in the full-text of the document, is a promising alternative means of capturing critical topics covered by journal articles. More specifically, this document representation strategy when used by the clustering algorithm investigated in this paper, outperforms both the full-text clustering approach and the link-based clustering technique on both scientific journal datasets.},
journal = {Inf. Retr.},
month = apr,
pages = {101–131},
numpages = {31},
keywords = {Document clustering, Text categorization, Citation contexts}
}

@article{10.1007/s10791-009-9107-y,
author = {Lu, Jianguo and Li, Dingding},
title = {Estimating Deep Web Data Source Size by Capture---Recapture Method},
year = {2010},
issue_date = {February  2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9107-y},
doi = {10.1007/s10791-009-9107-y},
abstract = {This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture---recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation.},
journal = {Inf. Retr.},
month = feb,
pages = {70–95},
numpages = {26},
keywords = {Deep web, Capture---recapture, Estimators}
}

@article{10.1007/s10791-009-9100-5,
author = {Stokes, Nicola},
title = {William Hersh: Information Retrieval: A Health and Biomedical Perspective, 3rd Ed},
year = {2010},
issue_date = {February  2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9100-5},
doi = {10.1007/s10791-009-9100-5},
journal = {Inf. Retr.},
month = feb,
pages = {96–100},
numpages = {5}
}

@article{10.1007/s10791-009-9099-7,
author = {Zhang, Yuye and Park, Laurence A. and Moffat, Alistair},
title = {Click-Based Evidence for Decaying Weight Distributions in Search Effectiveness Metrics},
year = {2010},
issue_date = {February  2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9099-7},
doi = {10.1007/s10791-009-9099-7},
abstract = {Search effectiveness metrics are used to evaluate the quality of the answer lists returned by search services, usually based on a set of relevance judgments. One plausible way of calculating an effectiveness score for a system run is to compute the inner-product of the run's relevance vector and a "utility" vector, where the ith element in the utility vector represents the relative benefit obtained by the user of the system if they encounter a relevant document at depth i in the ranking. This paper uses such a framework to examine the user behavior patterns--and hence utility weightings--that can be inferred from a web query log. We describe a process for extrapolating user observations from query log clickthroughs, and employ this user model to measure the quality of effectiveness weighting distributions. Our results show that for measures with static distributions (that is, utility weighting schemes for which the weight vector is independent of the relevance vector), the geometric weighting model employed in the rank-biased precision effectiveness metric offers the closest fit to the user observation model. In addition, using past TREC data as to indicate likelihood of relevance, we also show that the distributions employed in the BPref and MRR metrics are the best fit out of the measures for which static distributions do not exist.},
journal = {Inf. Retr.},
month = feb,
pages = {46–69},
numpages = {24},
keywords = {Clickthrough, Reciprocal rank, Query log, BPref, Rank-biased precision, Effectiveness metric, Average precision}
}

@article{10.1007/s10791-009-9098-8,
author = {Liu, Yuting and Liu, Tie-Yan and Gao, Bin and Ma, Zhiming and Li, Hang},
title = {A Framework to Compute Page Importance Based on User Behaviors},
year = {2010},
issue_date = {February  2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9098-8},
doi = {10.1007/s10791-009-9098-8},
abstract = {This paper is concerned with a framework to compute the importance of webpages by using real browsing behaviors of Web users. In contrast, many previous approaches like PageRank compute page importance through the use of the hyperlink graph of the Web. Recently, people have realized that the hyperlink graph is incomplete and inaccurate as a data source for determining page importance, and proposed using the real behaviors of Web users instead. In this paper, we propose a formal framework to compute page importance from user behavior data (which covers some previous works as special cases). First, we use a stochastic process to model the browsing behaviors of Web users. According to the analysis on hundreds of millions of real records of user behaviors, we justify that the process is actually a continuous-time time-homogeneous Markov process, and its stationary probability distribution can be used as the measure of page importance. Second, we propose a number of ways to estimate parameters of the stochastic process from real data, which result in a group of algorithms for page importance computation (all referred to as BrowseRank). Our experimental results have shown that the proposed algorithms can outperform the baseline methods such as PageRank and TrustRank in several tasks, demonstrating the advantage of using our proposed framework.},
journal = {Inf. Retr.},
month = feb,
pages = {22–45},
numpages = {24},
keywords = {BrowseRank, Continuous-time time-homogeneous Markov process, Staying time, User browsing process}
}

@article{10.1007/s10791-009-9097-9,
author = {Lemstr\"{o}m, Kjell and Mikkil\"{a}, Niko and M\"{a}kinen, Veli},
title = {Filtering Methods for Content-Based Retrieval on Indexed Symbolic Music Databases},
year = {2010},
issue_date = {February  2010},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {13},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9097-9},
doi = {10.1007/s10791-009-9097-9},
abstract = {We introduce fast filtering methods for content-based music retrieval problems, where the music is modeled as sets of points in the Euclidean plane, formed by the (on-set time, pitch) pairs. The filters exploit a precomputed index for the database, and run in time dependent on the query length and intermediate output sizes of the filters, being almost independent of the database size. With a quadratic size index, the filters are provably lossless for general point sets of this kind. In the context of music, the search space can be narrowed down, which enables the use of a linear sized index for effective and efficient lossless filtering. For the checking phase, which dominates the overall running time, we exploit previously designed algorithms suitable for local checking. In our experiments on a music database, our best filter-based methods performed several orders of a magnitude faster than the previously designed solutions.},
journal = {Inf. Retr.},
month = feb,
pages = {1–21},
numpages = {21},
keywords = {Indexing, Symbolically encoded polyphonic music, Filtering, Content-based retrieval}
}

@article{10.1007/s10791-009-9105-0,
author = {Clarke, Charles L. and Cormack, Gordon V. and Lynam, Thomas R. and Buckley, Chris and Harman, Donna},
title = {Swapping Documents and Terms},
year = {2009},
issue_date = {December  2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9105-0},
doi = {10.1007/s10791-009-9105-0},
abstract = {Experiments were conducted to explore the impact of combining various components of eight leading information retrieval systems. Each system demonstrated improved effectiveness through the use of  blind feedback , also known as  pseudo-relevance feedback , a form of query expansion. Blind feedback uses the results of a preliminary retrieval step to augment the efficacy of a secondary retrieval step. The hybrid combination of primary and secondary retrieval steps from different systems in a number of cases yielded better effectiveness than either of the constituent systems alone. This positive combining effect was observed when entire documents were passed between the two retrieval steps, but not when only the expansion terms were passed. Several combinations of primary and secondary retrieval steps were fused using the CombMNZ algorithm; all yielded significant effectiveness improvement over the individual systems, with the best yielding an improvement of 13% (  p  = 10 6) over the best individual system and an improvement of 4% (  p  = 10 5) over a simple fusion of the eight systems.},
journal = {Inf. Retr.},
month = dec,
pages = {680–694},
numpages = {15},
keywords = {Fusion, Pseudo-relevance feedback, Blink feedback}
}

@article{10.1007/s10791-009-9104-1,
author = {Ogilvie, Paul and Voorhees, Ellen and Callan, Jamie},
title = {On the Number of Terms Used in Automatic Query Expansion},
year = {2009},
issue_date = {December  2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9104-1},
doi = {10.1007/s10791-009-9104-1},
abstract = {This paper investigates the number of expansion terms to use in automatic query expansion by examining the behavior of eight retrieval systems participating in the NRRC Reliable Information Access Workshop. The results demonstrate that current systems are able to obtain nearly all of the benefit of using a fixed number of expansion terms per topic, but significant additional improvement is possible if systems were able to accurately select the best number of expansion terms on a per topic basis. When optimizing average effectiveness as measured by mean average precision, using a fixed number of terms increases the score a large amount for a small number of topics but has little effect for most topics. The analysis further suggests that when a topic is helped by automatic feedback, the increase is from a set of terms that reinforce each other rather than from the system finding a single excellent term.},
journal = {Inf. Retr.},
month = dec,
pages = {666–679},
numpages = {14},
keywords = {Automatic query expansion, Blind feedback, Pseudo relevance feedback}
}

@article{10.1007/s10791-009-9103-2,
author = {Buckley, Chris},
title = {Why Current IR Engines Fail},
year = {2009},
issue_date = {December  2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9103-2},
doi = {10.1007/s10791-009-9103-2},
abstract = {Observations from a unique investigation of failure analysis of Information Retrieval research engines held in 2003 are presented. The Reliable Information Access Workshop invited seven leading IR research groups to supply both their systems and their experts to an effort to analyze why their systems fail on some topics and whether the failures are due to system flaws, approach flaws, or the topic itself. There were surprising results from this cross-system failure analysis. One is that despite systems retrieving very different documents, the major cause of failure for any particular topic was almost always the same across all systems. Another is that relationships between aspects of a topic are not especially important for state-of-the-art systems; the systems are failing at a much more basic level where the top-retrieved documents are not reflecting some aspect at all. The investigatory framework and the lessons learned can serve as a model for needed future research in this area.},
journal = {Inf. Retr.},
month = dec,
pages = {652–665},
numpages = {14},
keywords = {Comparative system analysis, Failure analysis, Information retrieval evaluation, Failure categorization}
}

@article{10.1007/s10791-009-9102-3,
author = {Soboroff, Ian},
title = {A Guide to the RIA Workshop Data Archive},
year = {2009},
issue_date = {December  2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9102-3},
doi = {10.1007/s10791-009-9102-3},
abstract = {During the course of the Reliable Information Access (RIA) workshop, a data archive was created to hold the outputs of the many experiments being done. This archive was designed to serve both as an organizational structure to support the researchers at the workshop itself and as a public archive of experimental retrieval results. This article describes the structure of the data in the archive and the ways in which the data may be accessed.},
journal = {Inf. Retr.},
month = dec,
pages = {642–651},
numpages = {10},
keywords = {Archive, Experiment}
}

@article{10.1007/s10791-009-9101-4,
author = {Harman, Donna and Buckley, Chris},
title = {Overview of the Reliable Information Access Workshop},
year = {2009},
issue_date = {December  2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9101-4},
doi = {10.1007/s10791-009-9101-4},
abstract = {The Reliable Information Access (RIA) Workshop was held in the summer of 2003, with a goal of improved understanding of information retrieval systems, in particular with regard to the variability of retrieval performance across topics. The workshop ran massive cross-system failure analysis on 45 of the TREC topics and also performed cross-system experiments on pseudo-relevance feedback. This paper presents an overview of that workshop, along with some preliminary conclusions from these experiments. Even if this workshop was held 6 years ago, the issues of improving system performance across all topics is still critical to the field and this paper, along with the others in this issue, are the first widely published full papers for the workshop.},
journal = {Inf. Retr.},
month = dec,
pages = {615–641},
numpages = {27},
keywords = {Relevance feedback, Information retrieval, Failure analysis}
}

@article{10.1007/s10791-009-9106-z,
author = {Amig\.{o}, Enrique and Gonzalo, Julio and Artiles, Javier and Verdejo, Felisa},
title = {A Comparison of Extrinsic Clustering Evaluation Metrics Based on Formal Constraints},
year = {2009},
issue_date = {October   2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9106-z},
doi = {10.1007/s10791-009-9106-z},
journal = {Inf. Retr.},
month = oct,
pages = {613},
numpages = {1}
}

@article{10.1007/s10791-009-9096-x,
author = {Klampanos, Iraklis A.},
title = {Manning Christopher, Prabhakar Raghavan, Hinrich Sch\"{u}Tze: Introduction to Information Retrieval},
year = {2009},
issue_date = {October   2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9096-x},
doi = {10.1007/s10791-009-9096-x},
journal = {Inf. Retr.},
month = oct,
pages = {609–612},
numpages = {4}
}

@article{10.1007/s10791-009-9095-y,
author = {O'Brien, Heather L.},
title = {D. Nahl, D. Bilal (Eds.): Information and Emotion: The Emergent Affective Paradigm in Information Behaviour Research and Theory},
year = {2009},
issue_date = {October   2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9095-y},
doi = {10.1007/s10791-009-9095-y},
journal = {Inf. Retr.},
month = oct,
pages = {605–608},
numpages = {4}
}

@article{10.1007/s10791-009-9094-z,
author = {Thomas, Paul and Hawking, David},
title = {Server Selection Methods in Personal Metasearch: A Comparative Empirical Study},
year = {2009},
issue_date = {October   2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9094-z},
doi = {10.1007/s10791-009-9094-z},
abstract = {Server selection is an important subproblem in distributed information retrieval (DIR) but has commonly been studied with collections of more or less uniform size and with more or less homogeneous content. In contrast, realistic DIR applications may feature much more varied collections. In particular, personal metasearch--a novel application of DIR which includes all of a user's online resources--may involve collections which vary in size by several orders of magnitude, and which have highly varied data. We describe a number of algorithms for server selection, and consider their effectiveness when collections vary widely in size and are represented by imperfect samples. We compare the algorithms on a personal metasearch testbed comprising calendar, email, mailing list and web collections, where collection sizes differ by three orders of magnitude. We then explore the effect of collection size variations using four partitionings of the TREC ad hoc data used in many other DIR experiments. Kullback-Leibler divergence, previously considered poorly effective, performs better than expected in this application; other techniques thought to be effective perform poorly and are not appropriate for this problem. A strong correlation with size-based rankings for many techniques may be responsible.},
journal = {Inf. Retr.},
month = oct,
pages = {581–604},
numpages = {24},
keywords = {Distributed information retrieval, Server selection}
}

@article{10.1007/s10791-008-9071-y,
author = {Aiolli, Fabio and Cardin, Riccardo and Sebastiani, Fabrizio and Sperduti, Alessandro},
title = {Preferential Text Classification: Learning Algorithms and Evaluation Measures},
year = {2009},
issue_date = {October   2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9071-y},
doi = {10.1007/s10791-008-9071-y},
abstract = {In many applicative contexts in which textual documents are labelled with thematic categories, a distinction is made between the primary categories of a document, which represent the topics that are central to it, and its secondary categories, which represent topics that the document only touches upon. We contend that this distinction, so far neglected in text categorization research, is important and deserves to be explicitly tackled. The contribution of this paper is threefold. First, we propose an evaluation measure for this  preferential text categorization  task, whereby different kinds of misclassifications involving either primary or secondary categories have a different impact on effectiveness. Second, we establish several baseline results for this task on a well-known benchmark for patent classification in which the distinction between primary and secondary categories is present; these results are obtained by reformulating the preferential text categorization task in terms of well established classification problems, such as single and/or multi-label multiclass classification; state-of-the-art learning technology such as SVMs and kernel-based methods are used. Third, we improve on these results by using a recently proposed class of algorithms explicitly devised for learning from training data expressed in preferential form, i.e., in the form "for document  d    i  , category  c  is preferred to category  c  "; this allows us to distinguish between primary and secondary categories not only in the classification phase but also in the learning phase, thus differentiating their impact on the classifiers to be generated.},
journal = {Inf. Retr.},
month = oct,
pages = {559–580},
numpages = {22},
keywords = {Text classification, Primary and secondary categories, Supervised learning, Preferential learning, Text categorization}
}

@article{10.1007/s10791-008-9070-z,
author = {Boiy, Erik and Moens, Marie-Francine},
title = {A Machine Learning Approach to Sentiment Analysis in Multilingual Web Texts},
year = {2009},
issue_date = {October   2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9070-z},
doi = {10.1007/s10791-008-9070-z},
abstract = {Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83% accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 and 68% respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated.},
journal = {Inf. Retr.},
month = oct,
pages = {526–558},
numpages = {33},
keywords = {Information tracking, Cross-language learning, Opinion mining, Active learning}
}

@article{10.1007/s10791-008-9069-5,
author = {Wilbur, W. John and Kim, Won},
title = {The Ineffectiveness of Within-Document Term Frequency in Text Classification},
year = {2009},
issue_date = {October   2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9069-5},
doi = {10.1007/s10791-008-9069-5},
abstract = {For the purposes of classification it is common to represent a document as a bag of words. Such a representation consists of the individual terms making up the document together with the number of times each term appears in the document. All classification methods make use of the terms. It is common to also make use of the local term frequencies at the price of some added complication in the model. Examples are the na\"{\i}ve Bayes multinomial model (MM), the Dirichlet compound multinomial model (DCM) and the exponential-family approximation of the DCM (EDCM), as well as support vector machines (SVM). Although it is usually claimed that incorporating local word frequency in a document improves text classification performance, we here test whether such claims are true or not. In this paper we show experimentally that simplified forms of the MM, EDCM, and SVM models which ignore the frequency of each word in a document perform about at the same level as MM, DCM, EDCM and SVM models which incorporate local term frequency. We also present a new form of the na\"{\i}ve Bayes multivariate Bernoulli model (MBM) which is able to make use of local term frequency and show again that it offers no significant advantage over the plain MBM. We conclude that word burstiness is so strong that additional occurrences of a word essentially add no useful information to a classifier.},
journal = {Inf. Retr.},
month = oct,
pages = {509–525},
numpages = {17},
keywords = {Word burstiness, Within-document frequency, Bag-of-words}
}

@article{10.1007/s10791-008-9068-6,
author = {Amorim, Renato Cordeiro},
title = {Pascal Poncelet, Florent Masseglia, Maguelonne Teisseire: Successes and New Directions in Data Mining},
year = {2009},
issue_date = {August    2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9068-6},
doi = {10.1007/s10791-008-9068-6},
journal = {Inf. Retr.},
month = aug,
pages = {504–508},
numpages = {5}
}

@article{10.1007/s10791-008-9067-7,
author = {Lin, Jimmy and Wilbur, W. John},
title = {Modeling Actions of PubMed Users with N-Gram Language Models},
year = {2009},
issue_date = {August    2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9067-7},
doi = {10.1007/s10791-008-9067-7},
abstract = {Transaction logs from online search engines are valuable for two reasons: First, they provide insight into human information-seeking behavior. Second, log data can be used to train user models, which can then be applied to improve retrieval systems. This article presents a study of logs from PubMed®, the public gateway to the MEDLINE® database of bibliographic records from the medical and biomedical primary literature. Unlike most previous studies on general Web search, our work examines user activities with a highly-specialized search engine. We encode user actions as string sequences and model these sequences using n-gram language models. The models are evaluated in terms of perplexity and in a sequence prediction task. They help us better understand how PubMed users search for information and provide an enabler for improving users' search experience.},
journal = {Inf. Retr.},
month = aug,
pages = {487–503},
numpages = {17},
keywords = {Search behavior, Query log analysis}
}

@article{10.1007/s10791-008-9066-8,
author = {Amig\'{o}, Enrique and Gonzalo, Julio and Artiles, Javier and Verdejo, Felisa},
title = {A Comparison of Extrinsic Clustering Evaluation Metrics Based on Formal Constraints},
year = {2009},
issue_date = {August    2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9066-8},
doi = {10.1007/s10791-008-9066-8},
abstract = {There is a wide set of evaluation metrics available to compare the quality of text clustering algorithms. In this article, we define a few intuitive formal constraints on such metrics which shed light on which aspects of the quality of a clustering are captured by different metric families. These formal constraints are validated in an experiment involving human assessments, and compared with other constraints proposed in the literature. Our analysis of a wide range of metrics shows that only BCubed satisfies all formal constraints. We also extend the analysis to the problem of overlapping clustering, where items can simultaneously belong to more than one cluster. As Bcubed cannot be directly applied to this task, we propose a modified version of Bcubed that avoids the problems found with other metrics.},
journal = {Inf. Retr.},
month = aug,
pages = {461–486},
numpages = {26},
keywords = {Formal constraints, Clustering, Evaluation metrics}
}

@article{10.1007/s10791-008-9065-9,
author = {Kurland, Oren},
title = {Re-Ranking Search Results Using Language Models of Query-Specific Clusters},
year = {2009},
issue_date = {August    2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9065-9},
doi = {10.1007/s10791-008-9065-9},
abstract = {To obtain high precision at top ranks by a search performed in response to a query, researchers have proposed a cluster-based re-ranking paradigm: clustering an initial list of documents that are the most highly ranked by some initial search, and using information induced from these (often called) query-specific clusters for re-ranking the list. However, results concerning the effectiveness of various automatic cluster-based re-ranking methods have been inconclusive. We show that using query-specific clusters for automatic re-ranking of top-retrieved documents is effective with several methods in which clusters play different roles, among which is the smoothing of document language models. We do so by adapting previously-proposed cluster-based retrieval approaches, which are based on (static) query-independent clusters for ranking all documents in a corpus, to the re-ranking setting wherein clusters are query-specific. The best performing method that we develop outperforms both the initial document-based ranking and some previously proposed cluster-based re-ranking approaches; furthermore, this algorithm consistently outperforms a state-of-the-art pseudo-feedback-based approach. In further exploration we study the performance of cluster-based smoothing methods for re-ranking with various (soft and hard) clustering algorithms, and demonstrate the importance of clusters in providing context from the initial list through a comparison to using single documents to this end.},
journal = {Inf. Retr.},
month = aug,
pages = {437–460},
numpages = {24},
keywords = {Cluster-based re-ranking, Cluster-based smoothing, Cluster-based language models, Query-specific clusters}
}

@article{10.1007/s10791-009-9093-0,
author = {Lazarinis, Fotis and Vilares, Jes\'{u}s and Tait, John and Efthimiadis, Efthimis N.},
title = {Current Research Issues and Trends in Non-English Web Searching},
year = {2009},
issue_date = {June      2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9093-0},
doi = {10.1007/s10791-009-9093-0},
abstract = {With increasingly higher numbers of non-English language web searchers the problems of efficient handling of non-English Web documents and user queries are becoming major issues for search engines. The main aim of this review paper is to make researchers aware of the existing problems in monolingual non-English Web retrieval by providing an overview of open issues. A significant number of papers are reviewed and the research issues investigated in these studies are categorized in order to identify the research questions and solutions proposed in these papers. Further research is proposed at the end of each section.},
journal = {Inf. Retr.},
month = jun,
pages = {230–250},
numpages = {21},
keywords = {Encoding handling, Non-English retrieval, Indexing, Query log analysis, Stopwords, Web searching, Segmentation, Stemming, Language identification, Lemmatization}
}

@article{10.1007/s10791-009-9092-1,
author = {Eguchi, Koji and Croft, W. Bruce},
title = {Query Structuring and Expansion with Two-Stage Term Dependence for Japanese Web Retrieval},
year = {2009},
issue_date = {June      2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9092-1},
doi = {10.1007/s10791-009-9092-1},
abstract = {In this paper, we propose a new term dependence model for information retrieval, which is based on a theoretical framework using Markov random fields. We assume two types of dependencies of terms given in a query: (i) long-range dependencies that may appear for instance within a passage or a sentence in a target document, and (ii) short-range dependencies that may appear for instance within a compound word in a target document. Based on this assumption, our two-stage term dependence model captures both long-range and short-range term dependencies differently, when more than one compound word appear in a query. We also investigate how query structuring with term dependence can improve the performance of query expansion using a relevance model. The relevance model is constructed using the retrieval results of the structured query with term dependence to expand the query. We show that our term dependence model works well, particularly when using query structuring with compound words, through experiments using a 100-gigabyte test collection of web documents mostly written in Japanese. We also show that the performance of the relevance model can be significantly improved by using the structured query with our term dependence model.},
journal = {Inf. Retr.},
month = jun,
pages = {251–274},
numpages = {24},
keywords = {Term dependence, Japanese web retrieval, Structured queries}
}

@article{10.1007/s10791-009-9091-2,
author = {Lazarinis, Fotis and Vilares, Jesus and Tait, John and Efthimiadis, Efthimis N.},
title = {Introduction to the Special Issue on Non-English Web Retrieval},
year = {2009},
issue_date = {June      2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9091-2},
doi = {10.1007/s10791-009-9091-2},
journal = {Inf. Retr.},
month = jun,
pages = {227–229},
numpages = {3}
}

@article{10.1007/s10791-008-9086-4,
author = {Berendt, Bettina and Kralisch, Anett},
title = {A User-Centric Approach to Identifying Best Deployment Strategies for Language Tools: The Impact of Content and Access Language on Web User Behaviour and Attitudes},
year = {2009},
issue_date = {June      2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9086-4},
doi = {10.1007/s10791-008-9086-4},
abstract = {The number of Web users whose first language is not English continues to grow, as does the amount of content provided in languages other than English. This poses new challenges for actors on the Web, such as in which language(s) content should be offered, how search tools should deal with mono- and multilingual content, and how users can make the best use of navigation and search options, suited to their individual linguistic skills. How should these challenges be dealt with? Technological approaches to non-English (or in general, cross-language) Web search have made large progress; however, translation remains a hard problem. This precludes a low-cost but high-quality blanket all-language coverage of the whole Web. In this paper, we propose a user-centric approach to answering questions of where to best concentrate efforts and investments. Drawing on linguistic research, we describe data on the availability of content and access to it in first and second languages across the Web. We then present three studies that investigated the impact of the availability (or not) of first-language content and access forms on user behaviour and attitudes. The results indicate that non-English languages are under-represented on the Web and that this is partly due to content-creation, link-setting and link-following behaviour. They also show that user satisfaction is influenced both by the cognitive effort of searching and the availability of alternative information in that language. These findings suggest that more cross-language tools are desirable. However, they also indicate that context (such as user groups' domain expertise or site type) should be considered when tradeoffs between information quality and multilinguality need to be taken into account.},
journal = {Inf. Retr.},
month = jun,
pages = {380–399},
numpages = {20},
keywords = {Non-English, Non-native language, Web search, Information seeking, Native language, Web-site access}
}

@article{10.1007/s10791-008-9085-5,
author = {Piskorski, Jakub and Wieloch, Karol and Sydow, Marcin},
title = {On Knowledge-Poor Methods for Person Name Matching and Lemmatization for Highly Inflectional Languages},
year = {2009},
issue_date = {June      2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9085-5},
doi = {10.1007/s10791-008-9085-5},
abstract = {Web person search is one of the most common activities of Internet users. Recently, a vast amount of work on applying various NLP techniques for person name disambiguation in large web document collections has been reported, where the main focus was on English and few other major languages. This article reports on knowledge-poor methods for tackling person name matching and lemmatization in Polish, a highly inflectional language with complex person name declension paradigm. These methods apply mainly well-established string distance metrics, some new variants thereof, automatically acquired simple suffix-based lemmatization patterns and some combinations of the aforementioned techniques. Furthermore, we also carried out some initial experiments on deploying techniques that utilize the context, in which person names appear. Results of numerous experiments are presented. The evaluation carried out on a data set extracted from a corpus of on-line news articles revealed that achieving lemmatization accuracy figures greater than 90% seems to be difficult, whereas combining string distance metrics with suffix-based patterns results in 97.6---99% accuracy for the name matching task. Interestingly, no significant additional gain could be achieved through integrating some basic techniques, which try to exploit the local context the names appear in. Although our explorations were focused on Polish, we believe that the work presented in this article constitutes practical guidelines for tackling the same problem for other highly inflectional languages with similar phenomena.},
journal = {Inf. Retr.},
month = jun,
pages = {275–299},
numpages = {25},
keywords = {Highly inflectional languages, Person name matching, String distance metrics, Lemmatization}
}

@article{10.1007/s10791-008-9084-6,
author = {Efthimiadis, Efthimis N. and Malevris, Nicos and Kousaridas, Apostolos and Lepeniotou, Alexandra and Loutas, Nikos},
title = {Non-English Web Search: An Evaluation of Indexing and Searching the Greek Web},
year = {2009},
issue_date = {June      2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9084-6},
doi = {10.1007/s10791-008-9084-6},
abstract = {The study reports on a longitudinal and comparative evaluation of Greek language searching on the web. Ten engines, five global (A9, AltaVista, Google, MSN Search, and Yahoo!) and five Greek (Anazitisi, Ano-Kato, Phantis. Trinity, and Visto), were evaluated using (a) navigational queries in 2004 and 2006; and (b) by measuring the freshness of the search engine indices in 2005 and 2006. Homepage finding queries for known Greek organizations were created and searched. Queries included the name of the organization in its Greek and non-Greek, English or transliterated equivalent forms. The organizations represented ten categories: government departments, universities, colleges, travel agencies, museums, media (TV, radio, newspapers), transportation, and banks. The freshness of the indices was evaluated by examining the status of the returned URLs (live versus dead) from the navigational queries, and by identifying if the engines have indexed 32480 active (live) Greek domain URLs. Effectiveness measures included (a) qualitative assessment of how engines handle the Greek language; (b) precision at 10 documents (P@10); (c) mean reciprocal rank (MRR); (d) Navigational Query Discounted Cumulative Gain (NQ-DCG), a new heuristic evaluation measure; (e) response time; (f) the ratio of the dead URL links returned, (g) the presence or absence of URLs and the decay observed over the period of the study. The results report on which of the global and Greek search engines perform best; and if the performance achieved is good enough from a user's perspective.},
journal = {Inf. Retr.},
month = jun,
pages = {352–379},
numpages = {28},
keywords = {Evaluation, Search engines, Greek web, Homepage finding, Greek queries, Non-English web search, Navigational queries}
}

@article{10.1007/s10791-008-9083-7,
author = {Guzm\'{a}n-Cabrera, Rafael and Montes-Y-G\'{o}mez, Manuel and Rosso, Paolo and Villase\~{n}or-Pineda, Luis},
title = {Using the Web as Corpus for Self-Training Text Categorization},
year = {2009},
issue_date = {June      2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9083-7},
doi = {10.1007/s10791-008-9083-7},
abstract = {Most current methods for automatic text categorization are based on supervised learning techniques and, therefore, they face the problem of requiring a great number of training instances to construct an accurate classifier. In order to tackle this problem, this paper proposes a new semi-supervised method for text categorization, which considers the automatic extraction of unlabeled examples from the Web and the application of an enriched self-training approach for the construction of the classifier. This method, even though language independent, is more pertinent for scenarios where large sets of labeled resources do not exist. That, for instance, could be the case of several application domains in different non-English languages such as Spanish. The experimental evaluation of the method was carried out in three different tasks and in two different languages. The achieved results demonstrate the applicability and usefulness of the proposed method.},
journal = {Inf. Retr.},
month = jun,
pages = {400–415},
numpages = {16},
keywords = {Semi-supervised learning, Authorship attribution, Self-training, Web as corpus, Text categorization}
}

@article{10.1007/s10791-008-9082-8,
author = {Blanco, Roi and Lioma, Christina},
title = {Mixed Monolingual Homepage Finding in 34 Languages: The Role of Language Script and Search Domain},
year = {2009},
issue_date = {June      2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9082-8},
doi = {10.1007/s10791-008-9082-8},
abstract = {The information that is available or sought on the World Wide Web (Web) is increasingly multilingual. Information Retrieval systems, such as the freely available search engines on the Web, need to provide fair and equal access to this information, regardless of the language in which a query is written or where the query is posted from. In this work, we ask two questions: How do existing state of the art search engines deal with languages written in different alphabets (scripts)__ __ Do local language-based search domains actually facilitate access to information__ __ We conduct a thorough study on the effect of multilingual queries for homepage finding, where the aim of the retrieval system is to return only one document, namely the homepage described in the query. We evaluate the effect of multilingual queries in retrieval performance with regard to (i) the alphabet in which the queries are written (e.g., Latin, Russian, Arabic), and (ii) the language domain where the queries are posted (e.g., google.com, google.fr). We query four major freely available search engines with 764 queries in 34 different languages, and look for the correct homepage in the top retrieved results. In order to have fair multilingual experimental settings, we use an ontology that is comparable across languages and also representative of realistic Web searches: football premier leagues in different countries; the official team name represents our query, and the official team homepage represents the document to be retrieved. A series of thorough experiments involving over 10,000 runs, with queries both in their correct and in Latin characters, and also using both global-domain and local-domain searches, reveal that queries issued in the correct script of a language are more likely to be found and ranked in the top 3, while queries in non-Latin script languages which are however issued in Latin script are less likely to be found; also, queries issued to the correct local domain of a search engine, e.g., French queries to yahoo.fr, are likely to have better retrieval performance than queries issued to the global domain of a search engine. To our knowledge, this is the first Web retrieval study that uses such a wide range of languages.},
journal = {Inf. Retr.},
month = jun,
pages = {324–351},
numpages = {28},
keywords = {Web information retrieval, Multilingual information retrieval, Search engines and evaluation}
}

@article{10.1007/s10791-008-9081-9,
author = {Hammo, Bassam H.},
title = {Towards Enhancing Retrieval Effectiveness of Search Engines for Diacritisized Arabic Documents},
year = {2009},
issue_date = {June      2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9081-9},
doi = {10.1007/s10791-008-9081-9},
abstract = {The majority of Arabic text available on the web is written without short vowels (diacritics). Diacritics are commonly used in religious scripts such as the holy Quran (the book of Islam), Al-Hadith (the teachings of Prophet Mohammad (PBUH)), children's literature, and in some words where ambiguity of articulation might arise. Internet Arabic users might lose credible sources of Arabic text to be retrieved if they could not match the correct diacritical marks attached to the words in the collection. However, typing the diacritical marks is very annoying and time consuming. The other way around, is to ignore these marks and fall into the problem of ambiguity. Previous work suggested pre-processing of Arabic text to remove these diacritical marks before indexing. Consequently, there are noticeable discrepancies when searching the web for Arabic text using international search engines such as Google and yahoo. In this article, we propose a framework to enhance the retrieval effectiveness of search engines to search for diacritic and diacritic-less Arabic text through query expansion techniques. We used a rule-based stemmer and a semantic relational database compiled in an experimental thesaurus to do the expansion. We tested our approach on the scripts of the Quran. We found that query expansion for searching Arabic text is promising and it is likely that the efficiency can be further improved by advanced natural language processing tools.},
journal = {Inf. Retr.},
month = jun,
pages = {300–323},
numpages = {24},
keywords = {Arabic stemming, Arabic information retrieval, Query expansion, Thesaurus, Diacritisized scripts}
}

@article{10.1007/s10791-008-9080-x,
author = {Asker, Lars and Argaw, Atelach Alemu and Gamb\"{a}ck, Bj\"{o}rn and Eyassu Asfeha, Samuel and Nigussie Habte, Lemma},
title = {Classifying Amharic Webnews},
year = {2009},
issue_date = {June      2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9080-x},
doi = {10.1007/s10791-008-9080-x},
abstract = {We present work aimed at compiling an Amharic corpus from the Web and automatically categorizing the texts. Amharic is the second most spoken Semitic language in the World (after Arabic) and used for countrywide communication in Ethiopia. It is highly inflectional and quite dialectally diversified. We discuss the issues of compiling and annotating a corpus of Amharic news articles from the Web. This corpus was then used in three sets of text classification experiments. Working with a less-researched language highlights a number of practical issues that might otherwise receive less attention or go unnoticed. The purpose of the experiments has not primarily been to develop a cutting-edge text classification system for Amharic, but rather to put the spotlight on some of these issues. The first two sets of experiments investigated the use of Self-Organizing Maps (SOMs) for document classification. Testing on small datasets, we first looked at classifying unseen data into 10 predefined categories of news items, and then at clustering it around query content, when taking 16 queries as class labels. The second set of experiments investigated the effect of operations such as stemming and part-of-speech tagging on text classification performance. We compared three representations while constructing classification models based on bagging of decision trees for the 10 predefined news categories. The best accuracy was achieved using the full text as representation. A representation using only the nouns performed almost equally well, confirming the assumption that most of the information required for distinguishing between various categories actually is contained in the nouns, while stemming did not have much effect on the performance of the classifier.},
journal = {Inf. Retr.},
month = jun,
pages = {416–435},
numpages = {20},
keywords = {Text classification, Semitic languages, Web mining}
}

@article{10.1007/s10791-009-9090-3,
author = {Fern\'{a}ndez-Luna, Juan M. and Huete, Juan F. and Macfarlane, Andrew},
title = {Introduction to the Special Issue on Teaching and Learning in Information Retrieval},
year = {2009},
issue_date = {April     2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9090-3},
doi = {10.1007/s10791-009-9090-3},
abstract = {We present an overview of the special issue in this paper. The main objective is to provide information for lecturers on how to improve the student experience, using current knowledge in the field. To this end we present an overview of six papers covering areas as diverse as tools and methods used to support teaching and learning, pedagogical challenges in teaching mathematics for search, etc.},
journal = {Inf. Retr.},
month = apr,
pages = {99–101},
numpages = {3},
keywords = {Learning, Educational assessment, Teaching, Curricula, Information retrieval}
}

@article{10.1007/s10791-009-9089-9,
author = {Fern\'{a}ndez-Luna, Juan M. and Huete, Juan F. and Macfarlane, Andrew and Efthimiadis, Efthimis N.},
title = {Teaching and Learning in Information Retrieval},
year = {2009},
issue_date = {April     2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9089-9},
doi = {10.1007/s10791-009-9089-9},
abstract = {A literature review of pedagogical methods for teaching and learning information retrieval is presented. From the analysis of the literature a taxonomy was built and it is used to structure the paper. Information Retrieval (IR) is presented from different points of view: technical levels, educational goals, teaching and learning methods, assessment and curricula. The review is organized around two levels of abstraction which form a taxonomy that deals with the different aspects of pedagogy as applied to information retrieval. The first level looks at the technical level of delivering information retrieval concepts, and at the educational goals as articulated by the two main subject domains where IR is delivered: computer science (CS) and library and information science (LIS). The second level focuses on pedagogical issues, such as teaching and learning methods, delivery modes (classroom, online or e-learning), use of IR systems for teaching, assessment and feedback, and curricula design. The survey, and its bibliography, provides an overview of the pedagogical research carried out in the field of IR. It also provides a guide for educators on approaches that can be applied to improving the student learning experiences.},
journal = {Inf. Retr.},
month = apr,
pages = {201–226},
numpages = {26},
keywords = {Learning, Curricula, Teaching, Educational assessment, Information retrieval}
}

@article{10.1007/s10791-009-9088-x,
author = {Jones, Gareth J.},
title = {An Inquiry-Based Learning Approach to Teaching Information Retrieval},
year = {2009},
issue_date = {April     2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-009-9088-x},
doi = {10.1007/s10791-009-9088-x},
abstract = {The study of information retrieval (IR) has increased in interest and importance with the explosive growth of online information in recent years. Learning about IR within formal courses of study enables users of search engines to use them more knowledgeably and effectively, while providing the starting point for the explorations of new researchers into novel search technologies. Although IR can be taught in a traditional manner of formal classroom instruction with students being led through the details of the subject and expected to reproduce this in assessment, the nature of IR as a topic makes it an ideal subject for inquiry-based learning approaches to teaching. In an inquiry-based learning approach students are introduced to the principles of a subject and then encouraged to develop their understanding by solving structured or open problems. Working through solutions in subsequent class discussions enables students to appreciate the availability of alternative solutions as proposed by their classmates. Following this approach students not only learn the details of IR techniques, but significantly, naturally learn to apply them in solution of problems. In doing this they not only gain an appreciation of alternative solutions to a problem, but also how to assess their relative strengths and weaknesses. Developing confidence and skills in problem solving enables student assessment to be structured around solution of problems. Thus students can be assessed on the basis of their understanding and ability to apply techniques, rather simply their skill at reciting facts. This has the additional benefit of encouraging general problem solving skills which can be of benefit in other subjects. This approach to teaching IR was successfully implemented in an undergraduate module where students were assessed in a written examination exploring their knowledge and understanding of the principles of IR and their ability to apply them to solving problems, and a written assignment based on developing an individual research proposal.},
journal = {Inf. Retr.},
month = apr,
pages = {148–161},
numpages = {14},
keywords = {Teaching information retrieval, Inquiry-based learning, Language technology integration}
}

@article{10.1007/s10791-008-9087-3,
author = {Herrera-Viedma, E. and L\'{o}pez-Herrera, A. G. and Alonso, S. and Moreno, J. M. and Cabrerizo, F. J. and Porcel, C.},
title = {A Computer-Supported Learning System to Help Teachers to Teach Fuzzy Information Retrieval Systems},
year = {2009},
issue_date = {April     2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9087-3},
doi = {10.1007/s10791-008-9087-3},
abstract = {This paper describes a computer-supported learning system to teach students the principles and concepts of Fuzzy Information Retrieval Systems based on weighted queries. This tool is used to support the teacher's activity in the degree course Information Retrieval Systems Based on Artificial Intelligence at the Faculty of Library and Information Sciences at the University of Granada. Learning of languages of weighted queries in Fuzzy Information Retrieval Systems is complex because it is very difficult to understand the different semantics that could be associated to the weights of queries together with their respective strategies of query evaluation. We have developed and implemented this computer-supported education system because it allows to support the teacher's activity in the classroom to teach the use of weighted queries in FIRSs and it helps students to develop self-learning processes on the use of such queries. We have evaluated the performance of its use in the learning process according to the students' perceptions and their results obtained in the course's exams. We have observed that using this software tool the students learn better the management of the weighted query languages and then their performance in the exams is improved.},
journal = {Inf. Retr.},
month = apr,
pages = {179–200},
numpages = {22},
keywords = {Fuzzy connectives, Education, Weighted queries, Fuzzy Information Retrieval, Teaching}
}

@article{10.1007/s10791-008-9079-3,
author = {Henrich, Andreas and Sieber, Stefanie},
title = {Blended Learning and Pure E-Learning Concepts for Information Retrieval: Experiences and Future Directions},
year = {2009},
issue_date = {April     2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9079-3},
doi = {10.1007/s10791-008-9079-3},
abstract = {Today, teaching and learning are mostly supported by digital material and electronic communication ranging from the provision of slides or scripts in digital form to elaborate, interactive learning environments. This article describes the prospects and risks of blended learning and e-learning for information retrieval courses. It deals with adequate content presentation and representation, as well as with interaction concepts and didactic considerations concerning the cost-benefit ratio of animations, applets, and multimedia elements. We present lessons learnt from 6 years of teaching information retrieval in blended learning and pure e-learning scenarios, and derive graded concepts for basic and advanced topics based on a book-like content representation on the one side, and lecture-recordings on the other side. Each concept is complemented by a pragmatic and focussed use of auxiliary elements such as forums and self-tests. Examples for beneficial and misguided applets and animations are given, along with criteria for their differentiation. Finally, critical success factors for technology enhanced learning approaches in the information retrieval field are derived concerning the creation, utilisation, and maintenance of courses. In short, we will argue that taking into account the nature and stability of the presented content, as well as a thorough consideration of the affordable creation and maintenance effort, are crucial for the success of such concepts. In addition, the closer the concept is to pure e-learning, the more important a high digital presence of the lecturer becomes.},
journal = {Inf. Retr.},
month = apr,
pages = {117–147},
numpages = {31},
keywords = {Teaching information retrieval, Interaction, e-Learning, Blended learning}
}

@article{10.1007/s10791-008-9078-4,
author = {Macfarlane, Andrew},
title = {Teaching Mathematics for Search Using a Tutorial Style of Delivery},
year = {2009},
issue_date = {April     2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9078-4},
doi = {10.1007/s10791-008-9078-4},
abstract = {Understanding of mathematics is needed to underpin the process of search, either explicitly with Exact Match (Boolean logic, adjacency) or implicitly with Best match natural language search. In this paper we outline some pedagogical challenges in teaching mathematics for information retrieval (IR) to postgraduate information science students. The aim is to take these challenges either found by experience or in the literature, to identify both theoretical and practical ideas in order to improve the delivery of the material and positively affect the learning of the target audience by using a tutorial style of teaching. Results show that there is evidence to support the notion that a more pro-active style of teaching using tutorials yield benefits both in terms of assessment results and student satisfaction.},
journal = {Inf. Retr.},
month = apr,
pages = {162–178},
numpages = {17},
keywords = {Boolean logic, Information retrieval, Information science, Teaching}
}

@article{10.1007/s10791-008-9077-5,
author = {Vilar, Polona and \v{Z}umer, Maja},
title = {The Bologna Reform at the Department of Library and Information Science and Book Studies, University of Ljubljana},
year = {2009},
issue_date = {April     2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9077-5},
doi = {10.1007/s10791-008-9077-5},
abstract = {Relevance of the new Bologna study programme at the Department of Library and Information Science and Book Studies at University of Ljubljana is demonstrated from different aspects. The example of IS&R themes and topics which are recognized as one of LIS core areas is used to show the differences from the previous study programme in terms of content, together with coverage of Web 2.0 related themes. In regard to teaching and learning methods it is shown how e-learning is used to support the educational process. At the end a few insights into employability of future graduates are added.},
journal = {Inf. Retr.},
month = apr,
pages = {102–116},
numpages = {15},
keywords = {Information seeking and retrieval, Curriculum, Slovenia, E-learning, Web 2.0, LIS education, Ljubljana, Employability, Bologna reform}
}

@article{10.1007/s10791-008-9076-6,
author = {Hersh, William and Voorhees, Ellen},
title = {TREC Genomics Special Issue Overview},
year = {2009},
issue_date = {February  2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9076-6},
doi = {10.1007/s10791-008-9076-6},
journal = {Inf. Retr.},
month = feb,
pages = {1–15},
numpages = {15}
}

@article{10.1007/s10791-008-9075-7,
author = {Lu, Yue and Fang, Hui and Zhai, Chengxiang},
title = {An Empirical Study of Gene Synonym Query Expansion in Biomedical Information Retrieval},
year = {2009},
issue_date = {February  2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9075-7},
doi = {10.1007/s10791-008-9075-7},
abstract = {Due to the heavy use of gene synonyms in biomedical text, people have tried many query expansion techniques using synonyms in order to improve performance in biomedical information retrieval. However, mixed results have been reported. The main challenge is that it is not trivial to assign appropriate weights to the added gene synonyms in the expanded query; under-weighting of synonyms would not bring much benefit, while overweighting some unreliable synonyms can hurt performance significantly. So far, there has been no systematic evaluation of various synonym query expansion strategies for biomedical text. In this work, we propose two different strategies to extend a standard language modeling approach for gene synonym query expansion and conduct a systematic evaluation of these methods on all the available TREC biomedical text collections for ad hoc document retrieval. Our experiment results show that synonym expansion can significantly improve the retrieval accuracy. However, different query types require different synonym expansion methods, and appropriate weighting of gene names and synonym terms is critical for improving performance.},
journal = {Inf. Retr.},
month = feb,
pages = {51–68},
numpages = {18},
keywords = {Synonym query expansion, Language modeling, Biomedical information retrieval}
}

@article{10.1007/s10791-008-9074-8,
author = {Lu, Zhiyong and Kim, Won and Wilbur, W. John},
title = {Evaluation of Query Expansion Using MeSH in PubMed},
year = {2009},
issue_date = {February  2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9074-8},
doi = {10.1007/s10791-008-9074-8},
abstract = {This paper investigates the effectiveness of using MeSH® in PubMed through its automatic query expansion process: Automatic Term Mapping (ATM). We run Boolean searches based on a collection of 55 topics and about 160,000 MEDLINE® citations used in the 2006 and 2007 TREC Genomics Tracks. For each topic, we first automatically construct a query by selecting keywords from the question. Next, each query is expanded by ATM, which assigns different search tags to terms in the query. Three search tags: [MeSH Terms], [Text Words], and [All Fields] are chosen to be studied after expansion because they all make use of the MeSH field of indexed MEDLINE citations. Furthermore, we characterize the two different mechanisms by which the MeSH field is used. Retrieval results using MeSH after expansion are compared to those solely based on the words in MEDLINE title and abstracts. The aggregate retrieval performance is assessed using both F-measure and mean rank precision. Experimental results suggest that query expansion using MeSH in PubMed can generally improve retrieval performance, but the improvement may not affect end PubMed users in realistic situations.},
journal = {Inf. Retr.},
month = feb,
pages = {69–80},
numpages = {12},
keywords = {TREC genomics, Query expansion, PubMed, MeSH}
}

@article{10.1007/s10791-008-9073-9,
author = {Stokes, Nicola and Li, Yi and Cavedon, Lawrence and Zobel, Justin},
title = {Exploring Criteria for Successful Query Expansion in the Genomic Domain},
year = {2009},
issue_date = {February  2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9073-9},
doi = {10.1007/s10791-008-9073-9},
abstract = {Query Expansion is commonly used in Information Retrieval to overcome vocabulary mismatch issues, such as synonymy between the original query terms and a relevant document. In general, query expansion experiments exhibit mixed results. Overall TREC Genomics Track results are also mixed; however, results from the top performing systems provide strong evidence supporting the need for expansion. In this paper, we examine the conditions necessary for optimal query expansion performance with respect to two system design issues: IR framework and knowledge source used for expansion. We present a query expansion framework that improves Okapi baseline passage MAP performance by 185%. Using this framework, we compare and contrast the effectiveness of a variety of biomedical knowledge sources used by TREC 2006 Genomics Track participants for expansion. Based on the outcome of these experiments, we discuss the success factors required for effective query expansion with respect to various sources of term expansion, such as corpus-based cooccurrence statistics, pseudo-relevance feedback methods, and domain-specific and domain-independent ontologies and databases. Our results show that choice of document ranking algorithm is the most important factor affecting retrieval performance on this dataset. In addition, when an appropriate ranking algorithm is used, we find that query expansion with domain-specific knowledge sources provides an equally substantive gain in performance over a baseline system.},
journal = {Inf. Retr.},
month = feb,
pages = {17–50},
numpages = {34},
keywords = {Corpus based query expansion, Passage retrieval for genomic queries, Knowledge based query expansion, TREC 2006 Genomics Track, Concept-based normalisation passage ranking, Pseudo relevance feedback}
}

@article{10.1007/s10791-008-9072-x,
author = {Roberts, Phoebe M. and Cohen, Aaron M. and Hersh, William R.},
title = {Tasks, Topics and Relevance Judging for the TREC Genomics Track: Five Years of Experience Evaluating Biomedical Text Information Retrieval Systems},
year = {2009},
issue_date = {February  2009},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {12},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-008-9072-x},
doi = {10.1007/s10791-008-9072-x},
abstract = {With the help of a team of expert biologist judges, the TREC Genomics track has generated four large sets of "gold standard" test collections, comprised of over a hundred unique topics, two kinds of ad hoc retrieval tasks, and their corresponding relevance judgments. Over the years of the track, increasingly complex tasks necessitated the creation of judging tools and training guidelines to accommodate teams of part-time short-term workers from a variety of specialized biological scientific backgrounds, and to address consistency and reproducibility of the assessment process. Important lessons were learned about factors that influenced the utility of the test collections including topic design, annotations provided by judges, methods used for identifying and training judges, and providing a central moderator "meta-judge".},
journal = {Inf. Retr.},
month = feb,
pages = {81–97},
numpages = {17},
keywords = {Information retrieval, Text mining, Reference standards, Evaluation, Inter-annotator agreement}
}

@article{10.1007/s10791-008-9062-z,
author = {Luk, Robert W.},
title = {On Event Space and Rank Equivalence between Probabilistic Retrieval Models},
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/s10791-008-9062-z},
doi = {10.1007/s10791-008-9062-z},
abstract = {This paper discusses various issues about the rank equivalence of Lafferty and Zhai between the log-odds ratio and the query likelihood of probabilistic retrieval models. It highlights that Robertson's concerns about this equivalence may arise when multiple probability distributions are assumed to be uniformly distributed, after assuming that the marginal probability logically follows from Kolmogorov's probability axioms. It also clarifies that there are two types of rank equivalence relations between probabilistic models, namely strict and weak rank equivalence. This paper focuses on the strict rank equivalence which requires the event spaces of the participating probabilistic models to be identical. It is possible that two probabilistic models are strict rank equivalent when they use different probability estimation methods. This paper shows that the query likelihood,  p (  q |  d ,  r ), is strict rank equivalent to  p (  q |  d ) of the language model of Ponte and Croft by applying assumptions 1 and 2 of Lafferty and Zhai. In addition, some statistical component language model may be strict rank equivalent to the log-odds ratio, and that some statistical component model using the log-odds ratio may be strict rank equivalent to the query likelihood. Finally, we suggest adding a random variable for the user information need to the probabilistic retrieval models for clarification when these models deal with multiple requests.},
journal = {Inf. Retr.},
month = dec,
pages = {539–561},
numpages = {23},
keywords = {Information retrieval, Event space, Probabilistic models}
}

@article{10.1007/s10791-008-9061-0,
author = {Rokach, Lior and Romano, Roni and Maimon, Oded},
title = {Negation Recognition in Medical Narrative Reports},
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/s10791-008-9061-0},
doi = {10.1007/s10791-008-9061-0},
abstract = {Substantial medical data, such as discharge summaries and operative reports are stored in electronic textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are such. When searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. Hence, negation is a major source of poor precision in medical information retrieval systems. Previous research has shown that negated findings may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We present a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task, and show its advantages: accuracy improvement compared to other machine learning methods, and much faster than manual knowledge engineering techniques with matching accuracy. The new algorithm can be applied also to further context identification and information extraction tasks.},
journal = {Inf. Retr.},
month = dec

license_type: permissive

branch_name: refs/heads/main

extension: bib