text:
10.1145/1390334.1390522},
abstract = {Language modeling approaches have been effectively dealing with the dependency among query terms based on N-gram such as bigram or trigram models. However, bigram language models suffer from adjacency-sparseness problem which means that dependent terms are not always adjacent in documents, but can be far from each other, sometimes with distance of a few sentences in a document. To resolve the adjacency-sparseness problem, this paper proposes a new type of bigram language model by explicitly incorporating the proximity feature between two adjacent terms in a query. Experimental results on three test collections show that the proposed bigram language model significantly improves previous bigram model as well as Tao's approach, the state-of-art method for proximity-based method.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {821–822},
numpages = {2},
keywords = {language models, proximity, term dependency, bigram moel},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390523,
author = {Trieschnigg, Dolf and Meij, Edgar and de Rijke, Maarten and Kraaij, Wessel},
title = {Measuring Concept Relatedness Using Language Models},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390523},
doi = {10.1145/1390334.1390523},
abstract = {Over the years, the notion of concept relatedness has attracted considerable attention. A variety of approaches, based on ontology structure, information content, association, or context have been proposed to indicate the relatedness of abstract ideas. We propose a method based on the cross entropy reduction between language models of concepts which are estimated based on document-concept assignments. The approach shows improved or competitive results compared to state-of-the-art methods on two test sets in the biomedical domain.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {823–824},
numpages = {2},
keywords = {semantic relatedness, language models},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390524,
author = {Zighelnic, Liron and Kurland, Oren},
title = {Query-Drift Prevention for Robust Query Expansion},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390524},
doi = {10.1145/1390334.1390524},
abstract = {Pseudo-feedback-based automatic query expansion yields effective retrieval performance on average, but results in performance inferior to that of using the original query for many information needs. We address an important cause of this robustness issue, namely, the query drift problem, by fusing the results retrieved in response to the original query and to its expanded form. Our approach posts performance that is significantly better than that of retrieval based only on the original query and more robust than that of retrieval using the expanded query.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {825–826},
numpages = {2},
keywords = {robust query expansion, query drift, pseudo feedback, query expansion, fusion},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390525,
author = {Quan, Xiaojun and Chen, Enhong and Luo, Qiming and Xiong, Hui},
title = {Adaptive Label-Driven Scaling for Latent Semantic Indexing},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390525},
doi = {10.1145/1390334.1390525},
abstract = {This paper targets on enhancing Latent Semantic Indexing (LSI) by exploiting category labels. Specifically, in the term-document matrix, the vector for each term either appearing in labels or semantically close to labels is scaled before performing Singular Value Decomposition (SVD) to boost its impact on the generated left singular vectors. As a result, the similarities among documents in the same category are increased. Furthermore, an adaptive scaling strategy is designed to better utilize the hierarchical structure of categories. Experimental results show that the proposed approach is able to significantly improve the performance of hierarchical text categorization.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {827–828},
numpages = {2},
keywords = {hierarchical text categorization, latent semantic indexing, supervised latent semantic indexing},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390526,
author = {Lee, Changki and Kim, HyunKi and Jang, Myung-Gil},
title = {Fixed-Threshold SMO for Joint Constraint Learning Algorithm of Structural SVM},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390526},
doi = {10.1145/1390334.1390526},
abstract = {In this paper, we describe a fixed-threshold sequential minimal optimization (FSMO) for a joint constraint learning algorithm of structural classification SVM problems. Because FSMO uses the fact that the joint constraint formulation of structural SVM has b=0, FSMO breaks down the quadratic programming (QP) problems of structural SVM into a series of smallest QP problems, each involving only one variable. By using only one variable, FSMO is advantageous in that each QP sub-problem does not need subset selection.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {829–830},
numpages = {2},
keywords = {joint constraint learning algorithm, fixed-threshold SMO, structural SVM},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390527,
author = {Ding, Chris and Li, Tao and Luo, Dijun and Peng, Wei},
title = {Posterior Probabilistic Clustering Using NMF},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390527},
doi = {10.1145/1390334.1390527},
abstract = {We introduce the posterior probabilistic clustering (PPC), which provides a rigorous posterior probability interpretation for Nonnegative Matrix Factorization (NMF) and removes the uncertainty in clustering assignment. Furthermore, PPC is closely related to probabilistic latent semantic indexing (PLSI).},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {831–832},
numpages = {2},
keywords = {posterior probabilistic clustering, sparse, NMF},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390528,
author = {Goharian, Nazli and Mengle, Saket S.R.},
title = {On Document Splitting in Passage Detection},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390528},
doi = {10.1145/1390334.1390528},
abstract = {Passages can be hidden within a text to circumvent their disallowed transfer. Such release of compartmentalized information is of concern to all corporate and governmental organization. We explore the methodology to detect such hidden passages within a document. A document is divided into passages using various document splitting techniques, and a text classifier is used to categorize such passages. We present a novel document splitting technique called dynamic windowing, which significantly improves precision, recall and F1 measure.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {833–834},
numpages = {2},
keywords = {passage detection, text classification},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390529,
author = {Zhang, Dell and Lee, Wee Sun},
title = {Learning with Support Vector Machines for Query-by-Multiple-Examples},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390529},
doi = {10.1145/1390334.1390529},
abstract = {We explore an alternative Information Retrieval paradigm called Query-By-Multiple-Examples (QBME) where the information need is described not by a set of terms but by a set of documents. Intuitive ideas for QBME include using the centroid of these documents or the well-known Rocchio algorithm to construct the query vector. We consider this problem from the perspective of text classification, and find that a better query vector can be obtained through learning with Support Vector Machines (SVMs). For online queries, we show how SVMs can be learned from one-class examples in linear time. For offline queries, we show how SVMs can be learned from positive and unlabeled examples together in linear or polynomial time. The effectiveness and efficiency of the proposed approaches have been confirmed by our experiments on four real-world datasets.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {835–836},
numpages = {2},
keywords = {one-class learning, support vector machine, PU learning},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390530,
author = {Pan, Yan and Tang, Yong and Lin, Luxin and Luo, Yemin},
title = {Question Classification with Semantic Tree Kernel},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390530},
doi = {10.1145/1390334.1390530},
abstract = {Question Classification plays an important role in most Question Answering systems. In this paper, we exploit semantic features in Support Vector Machines (SVMs) for Question Classification. We propose a semantic tree kernel to incorporate semantic similarity information. A diverse set of semantic features is evaluated. Experimental results show that SVMs with semantic features, especially semantic classes, can significantly outperform the state-of-the-art systems.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {837–838},
numpages = {2},
keywords = {support vector machines, question answering, semantic class, machine learning, tree kernel, question classification},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390531,
author = {Thomas, Paul},
title = {Generalising Multiple Capture-Recapture to Non-Uniform Sample Sizes},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390531},
doi = {10.1145/1390334.1390531},
abstract = {Algorithms in distributed information retrieval often rely on accurate knowledge of the size of a collection. The "multiple capture-recapture" method of Shokouhi et al. is one of the more reliable algorithms for determining collection size, but it relies on samples with a uniform number of documents. Such uniform samples are often hard to obtain in a working system.A simple generalisation of multiple capture-recapture does not rely on uniform sample sizes. Simulations show it is as accurate as the original method even when sample sizes vary considerably, making it a useful technique in real tools.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {839–840},
numpages = {2},
keywords = {size estimation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390532,
author = {Rahurkar, Mandar and Cucerzan, Silviu},
title = {Predicting When Browsing Context is Relevant to Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390532},
doi = {10.1145/1390334.1390532},
abstract = {We investigate a representative case of sudden information need change of Web users. By analyzing search engine query logs, we show that the majority of queries submitted by users after browsing documents in the news domain are related to the most recently browsed document. We investigate ways of identifying whether a query is a good candidate for contextualization conditioned on the most recently browsed document by a user. We build a successful classifier for this task, which achieves 96% precision at 90% recall.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {841–842},
numpages = {2},
keywords = {contextualized web search, query log analysis, query expansion},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390534,
author = {Lehtonen, Miro and Doucet, Antoine},
title = {XML-Aided Phrase Indexing for Hypertext Documents},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390534},
doi = {10.1145/1390334.1390534},
abstract = {We combine techniques of XML Mining and Text Mining for the benefit of Information Retrieval. By manipulating the word sequence according to the XML structure of the marked-up text, we strengthen phrase boundaries so that they are more obvious to the algorithms that extract multiword sequences from text. Consequently, the quality of the indexed phrases improves, which has a positive effect on the average precision measured by the INEX 2007 standards.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {843–844},
numpages = {2},
keywords = {XML, text mining, word sequence, phrase, XML retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390535,
author = {Broschart, Andreas and Schenkel, Ralf},
title = {Proximity-Aware Scoring for XML Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390535},
doi = {10.1145/1390334.1390535},
abstract = {Proximity-aware scoring functions lead to significant effectiveness improvements for text retrieval. For XML IR, we can sometimes enhance the retrieval quality by exploiting knowledge about the document structure combined with established text IR methods. This paper introduces modified proximity scores that take the document structure into account and demonstrates the effect for the INEX benchmark.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {845–846},
numpages = {2},
keywords = {XML retrieval, proximity scoring},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390536,
author = {Kamps, Jaap and Koolen, Marijn and Lalmas, Mounia},
title = {Locating Relevant Text within XML Documents},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390536},
doi = {10.1145/1390334.1390536},
abstract = {Traditional document retrieval has shown to be a competitive approach in XML element retrieval, which is counter-intuitive since the element retrieval task requests all and only relevant document parts to be retrieved. This paper conducts a comparative analysis of document and element retrieval, highlights the relative strengths and weaknesses of both approaches, and explains the relative effectiveness of document retrieval approaches at element retrieval tasks.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {847–848},
numpages = {2},
keywords = {focused retrieval, passage retrieval, XML retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390537,
author = {Damiani, Ernesto and Marrara, Stefania and Pasi, Gabriella},
title = {A Flexible Extension of XPath to Improve XML Querying},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390537},
doi = {10.1145/1390334.1390537},
abstract = {This work presents a flexible XML selection language, FleXPath which allows the formulation of flexible constraints on both structure and content of XML documents. Some experimental results, obtained with a preliminary prototype, are described in order to show that the idea promises good results.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {849–850},
numpages = {2},
keywords = {XML retrieval, XPath, flexible query, fuzzy set theory},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390538,
author = {Rode, Henning and Serdyukov, Pavel and Hiemstra, Djoerd},
title = {Combining Document- and Paragraph-Based Entity Ranking},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390538},
doi = {10.1145/1390334.1390538},
abstract = {We study entity ranking on the INEX entity track and propose a simple graph-based ranking approach that enables to combine scores on document and paragraph level. The combined approach improves the retrieval results not only on the INEX testset, but similarly on TREC's expert finding task.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {851–852},
numpages = {2},
keywords = {graph-based retrieval, entity ranking, expert finding},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390539,
author = {Bendersky, Michael and Kurland, Oren},
title = {Re-Ranking Search Results Using Document-Passage Graphs},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390539},
doi = {10.1145/1390334.1390539},
abstract = {We present a novel passage-based approach to re-ranking documents in an initially retrieved list so as to improve precision at top ranks. While most work on passage-based document retrieval ranks a document based on the query similarity of its constituent passages, our approach leverages information about the centrality of the document passages with respect to the initial document list. Passage centrality is induced over a bipartite document-passage graph, wherein edge weights represent document-passage similarities. Empirical evaluation shows that our approach yields effective re-ranking performance. Furthermore, the performance is superior to that of previously proposed passage-based document ranking methods.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {853–854},
numpages = {2},
keywords = {passage-based document retrieval, passage-document graphs, passage language models, centrality},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390540,
author = {Xu, Yang and Ding, Fan and Wang, Bin},
title = {Utilizing Phrase Based Semantic Information for Term Dependency},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390540},
doi = {10.1145/1390334.1390540},
abstract = {Previous work on term dependency has not taken into account semantic information underlying query phrases. In this work, we study the impact of utilizing phrase based concepts for term dependency. We use Wikipedia to separate important and less important term dependencies, and treat them accordingly as features in a linear feature-based retrieval model. We compare our method with a Markov Random Field (MRF) model on four TREC document collections. Our experimental results show that utilizing phrase based concepts improves the retrieval effectiveness of term dependency, and reduces the size of the feature set to large extent.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {855–856},
numpages = {2},
keywords = {information retrieval, Markov random field, Wikipedia, phrase, term dependency},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390541,
author = {Vallet, David and Zaragoza, Hugo},
title = {Inferring the Most Important Types of a Query: A Semantic Approach},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390541},
doi = {10.1145/1390334.1390541},
abstract = {In this paper we present a technique for ranking the most important types or categories for a given query. Rather than trying to find the category of the query, known as query categorization, our approach seeks to find the most important types related to the query results. Not necessarily the query category falls into this ranking of types and therefore our approach can be complementary.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {857–858},
numpages = {2},
keywords = {entity ranking, type ranking, faceted search},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390542,
author = {Merhav, Yuval and Frieder, Ophir},
title = {On Multiword Entity Ranking in Peer-to-Peer Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390542},
doi = {10.1145/1390334.1390542},
abstract = {Previously [2], we postulated the advantage of using entity extraction to implement a new Peer-to-Peer (P2P) search framework for reducing network traffic and providing a trade off between precision and recall. We now propose an entity ranking method designed for the 'short documents' characteristic of P2P, which significantly improves both precision and recall in 'top results' P2P search. We construct a dynamic entity corpus using n-grams statistics and metadata, study its reliability, and use it to identify correlations between user query terms.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {859–860},
numpages = {2},
keywords = {entity ranking, peer-to-peer},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390543,
author = {Altingovde, Ismail Sengor and Demir, Engin and Can, Fazli and Ulusoy, \"{O}zg\"{u}r},
title = {Site-Based Dynamic Pruning for Query Processing in Search Engines},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390543},
doi = {10.1145/1390334.1390543},
abstract = {Web search engines typically index and retrieve at the page level. In this study, we investigate a dynamic pruning strategy that allows the query processor to first determine the most promising websites and then proceed with the similarity computations for those pages only within these sites.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {861–862},
numpages = {2},
keywords = {dynamic query pruning, cluster-skipping, inverted index},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390544,
author = {Banchs, Rafael E. and Kaltenbrunner, Andreas},
title = {Exploiting MDS Projections for Cross-Language IR},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390544},
doi = {10.1145/1390334.1390544},
abstract = {In this paper, we describe some preliminary work on using monolingual projections of document collections for performing cross-language information retrieval tasks. The proposed methodology uses multidimensional scaling for projecting the vector-space representations of a given multilingual document collection into spaces of lower dimensionality. An independent projection is computed for each different language, and the structural similarities of the resulting projections are exploited for information retrieval tasks.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {863–864},
numpages = {2},
keywords = {IR, cross-language, multidimensional scaling},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390545,
author = {Bar-Yossef, Ziv and Mashiach, Li-Tal},
title = {Local Approximation of PageRank and Reverse PageRank},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390545},
doi = {10.1145/1390334.1390545},
abstract = {We consider the problem of approximating the PageRank of a target node using only local information provided by a link server. We prove that local approximation of PageRank is feasible if and only if the graph has low in-degree and admits fast PageRank convergence. While natural graphs, such as the web graph, are abundant with high in-degree nodes, making local PageRank approximation too costly, we show that reverse natural graphs tend to have low indegree while maintaining fast PageRank convergence. It follows that calculating Reverse PageRank locally is frequently more feasible than computing PageRank locally. Finally, we demonstrate the usefulness of Reverse PageRank in five different applications.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {865–866},
numpages = {2},
keywords = {reverse PageRank, lower bounds, local approximation, PageRank},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390546,
author = {Banerjee, Somnath},
title = {Improving Text Classification Accuracy Using Topic Modeling over an Additional Corpus},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390546},
doi = {10.1145/1390334.1390546},
abstract = {The World Wide Web has many document repositories that can act as valuable sources of additional data for various machine learning tasks. In this paper, we propose a method of improving text classification accuracy by using such an additional corpus that can easily be obtained from the web. This additional corpus can be unlabeled and independent of the given classification task. The method proposed here uses topic modeling to extract a set of topics from the additional corpus. Those extracted topics then act as additional features of the data of the given classification task. An evaluation on the RCV1 dataset shows significant improvement over a baseline method.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {867–868},
numpages = {2},
keywords = {text classification, topic modeling, semi-supervised learning},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390547,
author = {Gkanogiannis, Anestis and Kalamboukis, Theodore},
title = {An Algorithm for Text Categorization},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390547},
doi = {10.1145/1390334.1390547},
abstract = {A novel and efficient learning algorithm is proposed for the binary linear classification problem. The algorithm is trained using the Rocchio's relevance feedback technique and builds a classifier by the intermediate hyperplane of two common tangent hyperplanes for the given category and its complement. Experimental results presented are very encouraging and justify the need for further research.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {869–870},
numpages = {2},
keywords = {text categorization, relevance feedback},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390548,
author = {Hu, Tianming and Xiong, Hui and Zhou, Wenjun and Sung, Sam Yuan and Luo, Hangzai},
title = {Hypergraph Partitioning for Document Clustering: A Unified Clique Perspective},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390548},
doi = {10.1145/1390334.1390548},
abstract = {Hypergraph partitioning has been considered as a promising method to address the challenges of high dimensionality in document clustering. With documents modeled as vertices and the relationship among documents captured by the hyperedges, the goal of graph partitioning is to minimize the edge cut. Therefore, the definition of hyperedges is vital to the clustering performance. While several definitions of hyperedges have been proposed, a systematic understanding of desired characteristics of hyperedges is still missing. To that end, in this paper, we first provide a unified clique perspective of the definition of hyperedges, which serves as a guide to define hyperedges. With this perspective, based on the concepts of hypercliques and shared (reverse) nearest neighbors, we propose three new types of clique hyperedges and analyze their properties regarding purity and size issues. Finally, we present an extensive evaluation using real-world document datasets. The experimental results show that, with shared (reverse) nearest neighbor based hyperedges, the clustering performance can be improved significantly in terms of various external validation measures without the need for fine tuning of parameters.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {871–872},
numpages = {2},
keywords = {document clustering, hypergraph partitioning, clique, shared nearest neighbor},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390549,
author = {Avrachenkov, Konstantin and Dobrynin, Vladimir and Nemirovsky, Danil and Pham, Son Kim and Smirnova, Elena},
title = {Pagerank Based Clustering of Hypertext Document Collections},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390549},
doi = {10.1145/1390334.1390549},
abstract = {Clustering hypertext document collection is an important task in Information Retrieval. Most clustering methods are based on document content and do not take into account the hyper-text links. Here we propose a novel PageRank based clustering (PRC) algorithm which uses the hypertext structure. The PRC algorithm produces graph partitioning with high modularity and coverage. The comparison of the PRC algorithm with two content based clustering algorithms shows that there is a good match between PRC clustering and content based clustering.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {873–874},
numpages = {2},
keywords = {PageRank based clustering, directed graphs},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390550,
author = {Kim, Seokhwan and Jeong, Minwoo and Lee, Gary Geunbae},
title = {An Alignment-Based Pattern Representation Model for Information Extraction},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390550},
doi = {10.1145/1390334.1390550},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {875–876},
numpages = {2},
keywords = {information extraction, pattern representation model},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390551,
author = {Zhang, Wei},
title = {Relational Distance-Based Collaborative Filtering},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390551},
doi = {10.1145/1390334.1390551},
abstract = {In this paper, we present a novel hybrid recommender system called RelationalCF, which integrate content and demographic information into a collaborative filtering framework by using relational distance computation approaches without the effort of form transformation and feature construction. Our experiments suggest that the effective combination of various kinds of information based on relational distance approaches provides improved accurate recommendations than other approaches.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {877–878},
numpages = {2},
keywords = {hybrid recommender system, collaborative filtering, relational distance},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390553,
author = {Gan, Keng Hoon and Keong, Phang Keat and Tan, Saravadee Sae and Kong, Tang Enya},
title = {Minexml: Bridging Unstructured Query with Structured Resources via Mediated Query},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390553},
doi = {10.1145/1390334.1390553},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {879},
numpages = {1},
keywords = {xml retrieval, search, query},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390554,
author = {Yasukawa, Michiko and Yokoo, Hidetoshi},
title = {Clustering Search Results for Mobile Terminals},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390554},
doi = {10.1145/1390334.1390554},
abstract = {Mobile terminals such as cell phones are much more restricted in terms of input/output functionality and, therefore, some special techniques must be incorporated to enable them to be easily used for Web searching. Further, searching for a location name is related to a dazzling variety of topics. We relate these two factors to each other to yield a new search system for map and text information. Presenting search results as clusters is helpful for users, especially in a mobile environment. The system makes mobile web searching easier and more efficient.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {880},
numpages = {1},
keywords = {web search},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390555,
author = {Sifer, Mark and Lin, Jian},
title = {Refining Search Results with Facet Landscapes},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390555},
doi = {10.1145/1390334.1390555},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {881},
numpages = {1},
keywords = {information visualization, faceted search, olap},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390556,
author = {Wu, Dan and He, Daqing},
title = {Ice-Tea: An Interactive Cross-Language Search Engine with Translation Enhancement},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390556},
doi = {10.1145/1390334.1390556},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {882},
numpages = {1},
keywords = {relevance feedback (rf), query expansion (qe), ice-tea, translation enhancement (te), clir},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390557,
author = {Fortuna, Bla\v{z} and Rupnik, Jan and Pajntar, Bo\v{s}tjan and Grobelnik, Marko and Mladeni\v{c}, Dunja},
title = {Cross-Lingual Search over 22 European Languages},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390557},
doi = {10.1145/1390334.1390557},
abstract = {In this paper we present a system for cross-lingual information retrieval, which can handle tens of languages and millions of documents. Functioning of the system is demonstrated on corpus of European Legislation (22 languages, more than 400,000 documents per language). The system uses an interactive web-interface, which can take advantage of a predefined thesaurus allowing the user to dynamically re-rank the retrieval results based on the mapping onto a predefined thesaurus.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {883},
numpages = {1},
keywords = {cross-lingual information retrieval, dynamic search ranking, search result visualization, canonical correlation analysis},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390558,
author = {Crecelius, Tom and Kacimi, Mouna and Michel, Sebastian and Neumann, Thomas and Parreira, Josiane X. and Schenkel, Ralf and Weikum, Gerhard},
title = {Social Recommendations at Work},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390558},
doi = {10.1145/1390334.1390558},
abstract = {Online communities have become popular for publishing and searching content, and also for connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. Items can be annotated and rated by different users, and users can connect to others that are usually friends and/or share common interests.We demonstrate a social recommendation system that takes advantages of users connections and tagging behavior to compute recommendations of items in such communities. The advantages can be verified via comparison to a standard IR technique.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {884},
numpages = {1},
keywords = {scoring and ranking, social networks},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390559,
author = {Can, Fazli and Kocberber, Seyit and Baglioglu, Ozgur and Kardas, Suleyman and Ocalan, Huseyin Cagdas and Uyar, Erkan},
title = {Bilkent News Portal: A Personalizable System with New Event Detection and Tracking Capabilities},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390559},
doi = {10.1145/1390334.1390559},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {885},
numpages = {1},
keywords = {web, news portal, new event detection and tracking},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390560,
author = {Larson, Ray R.},
title = {Geographic IR and Visualization in Time and Space},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390560},
doi = {10.1145/1390334.1390560},
abstract = {This demonstration will show how graphical geospatial query specifications can be used to obtain sets of georeferenced data ranked by probability of relevance, and displayed geographically and temporally in a geospatial browser with temporal support.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {886},
numpages = {1},
keywords = {geographic information retrieval algorithms, visualization},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390561,
author = {Pan, Hanglin and Schenkel, Ralf and Weikum, Gerhard},
title = {Fine-Grained Relevance Feedback for XML Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390561},
doi = {10.1145/1390334.1390561},
abstract = {This demonstration presents an XML IR system that allows users to give feedback of different granularities and types, using Dempster-Shafer theory of evidence to compute expanded and reweighted queries.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {887},
numpages = {1},
keywords = {XML IR, relevance feedback, dempster-shafer theory},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390562,
author = {West, Kris and Downie, J. Stephen and Hu, Xiao and Jones, M. Cameron},
title = {Dynamic Visualization of Music Classification Systems},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390562},
doi = {10.1145/1390334.1390562},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {888},
numpages = {1},
keywords = {classification, evaluation, music information retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390563,
author = {Murthy, Uma and Torres, Ricardo da Silva and Fox, Edward A. and Venkatachalam, Logambigai and Yang, Seungwon and Gon\c{c}alves, Marcos A.},
title = {From Concepts to Implementation and Visualization: Tools from a Team-Based Approach to Ir},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390563},
doi = {10.1145/1390334.1390563},
abstract = {Researchers have been studying and developing teaching materials for information retrieval (IR), such as [3]. Toolkits also have been built that provide hands-on experience to students. For example, IR-Toolbox [4] is an effort to close the gap between the students' understanding of IR concepts and real-life indexing and search systems. Such tools might be good for helping students in non-technical areas such as in the Library and Information Science field to develop their conceptual model of search engines. However, they do not cover emerging topics and skills, such as content-based image retrieval (CBIR) and fusion search. Although there is open source software (such as those in http://www.searchtools.com/tools/tools-opensource.html) that can be used to teach basic and advanced IR topics, they require a student to have high-level technical knowledge and to spend a long time to gain a practical understanding of these topics.We present a new and rapid approach to teach basic and advanced IR topics, such as text retrieval, web-based IR, CBIR, and fusion search, to Computer Science (CS) graduate students. We designed projects that would help students grasp the abovementioned IR topics. Students, working in teams, were given a practical application to start with -- the Superimposed Application for Image Description and Retrieval [5]. SAIDR (earlier, SIERRA) allows users to associate parts of images with multimedia information such as text annotations. Also, users may retrieve information in one of two 2 ways: (1) Perform text-based retrieval on annotations; (2) Perform CBIR on images and parts of images that look like a query image (or part of a query image).Each team was asked to build an enhancement for this application, involving text retrieval and/or CBIR, in three weeks time. The sub-projects are described in Table 1. The outcome of this activity was that students learned about IR concepts while being able to relate their applicability to a real world problem (Figure 1). Details of these projects may be found at http://collab.dlib.vt.edu/runwiki/wiki.pl?TabletPcImageRetrievalSuperimposedInformation. We will demonstrate the tools developed along with the IR concepts they illustrate (Table 1). We believe these tools may aid others to learn about basic and advanced topics in IR.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {889},
numpages = {1},
keywords = {information retrieval, education},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390565,
author = {Winter, Judith},
title = {Exploiting XML Structure to Improve Information Retrieval in Peer-to-Peer Systems},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390565},
doi = {10.1145/1390334.1390565},
abstract = {With the advent of XML as a standard for representation and exchange of structured documents, a growing amount of XML-documents are being stored in Peer-to-Peer (P2P) networks. Cur¬rent research on P2P search engines proposes the use of Informa¬tion Retrieval (IR) techniques to perform content-based search, but does not take into account structural features of documents.P2P systems typically have no central index, thus avoiding single-points-of-failures, but distribute all information among participating peers. Accordingly, a querying peer has only limited access to the index information and should select carefully which peers can help answering a given query by contributing resources such as local index information or CPU time for ranking computations. Bandwidth consumption is a major issue. To guarantee scalability, P2P systems have to reduce the number of peers involved in the retrieval process. As a result, the retrieval quality in terms of recall and precision may suffer substantially.In the proposed thesis, document structure is considered as an extra source of information to improve the retrieval quality of XML-documents in a P2P environment. The thesis centres on the following questions: how can structural information help to improve the retrieval of XML-documents in terms of result quality such as precision, recall, and specificity? Can XML structure support the routing of queries in distributed environments, especially the selection of promising peers? How can XML IR techniques be used in a P2P network while minimizing bandwidth consumption and considering performance aspects?To answer these questions and to analyze possible achievements, a search engine is proposed that exploits structural hints expressed explicitly by the user or implicitly by the self-describing structure of XML-documents. Additionally, more focused and specific results are obtained by providing ranked retrieval units that can be either XML-documents as a whole or the most relevant passages of theses documents. XML information retrieval techniques are applied in two ways: to select those peers participating in the retrieval process, and to compute the relevance of documents.The indexing approach includes both content and structural information of documents. To support efficient execution of multi term queries, index keys consist of rare combinations of (content, structure)-tuples. Performance is increased by using only fixedsized posting lists: frequent index keys are combined with each other iteratively until the new combination is rare, with a posting list size under a pre-set threshold. All posting lists are sorted by taking into account classical IR measures such as term frequency and inverted term frequency as well as weights for potential retrieval units of a document, with a slight bias towards documents on peers with good collections regarding the current index key and with good peer characteristics such as online times, available bandwidth, and latency.When extracting the posting list for a specific query, a re-ordering on the posting list is performed that takes into account the structural similarity between key and query. According to this preranking, peers are selected that are expected to hold information about potentially relevant documents and retrieval unitsThe final ranking is computed in parallel on those selected peers. The computation is based on an extension of the vector space model and distinguishes between weights for different structures of the same content. This allows weighting XML elements with respect to their discriminative power, e.g. a title will be weighted much higher than a footnote. Additionally, relevance is computed as a mixture of content relevance and structural similarity between a given query and a potential retrieval unit.Currently, a first prototype for P2P Information Retrieval of XML-documents called SPIRIX is being implemented. Experiments to evaluate the proposed techniques and use of structural hints will be performed on a distributed version of the INEX Wikipedia Collection.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {890},
numpages = {1},
keywords = {information retrieval, content-based XML-retrieval, distributed search, Peer-to-Peer, XML information retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390566,
author = {Arapakis, Ioannis},
title = {Affective Feedback: An Investigation into the Role of Emotions in the Information Seeking Process},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390566},
doi = {10.1145/1390334.1390566},
abstract = {User feedback is considered to be a critical element in the information seeking process. An important aspect of the feedback cycle is relevance assessment that has progressively become a popular practice in web searching activities and interactive information retrieval (IR). The value of relevance assessment lies in the disambiguation of the user's information need, which is achieved by applying various feedback techniques. Such techniques vary from explicit to implicit and help determine the relevance of the retrieved documents.The former type of feedback is usually obtained through the explicit and intended indication of documents as relevant (positive feedback) or irrelevant (negative feedback). Explicit feedback is a robust method for improving a system's overall retrieval performance and producing better query reformulations [1], at the expense of users' cognitive resources. On the other hand, implicit feedback techniques tend to collect information on search behavior in a more intelligent and unobtrusive manner. By doing so, they disengage the users from the cognitive burden of document rating and relevance judgments. Information-seeking activities such as reading time, saving, printing, selecting and referencing have been all treated as indicators of relevance, despite the lack of sufficient evidence to support their effectiveness [2].Besides their apparent differences, both categories of feedback techniques determine document relevance with respect to the cognitive and situational levels of the interactive dialogue that occurs between the user and the retrieval system [5]. However, this approach does not account for the dynamic interplay and adaptation that takes place between the different dialogue levels, but most importantly it does not consider the affective dimension of interaction. Users interact with intentions, motivations and feelings apart from real-life problems and information objects, which are all critical aspects of cognition and decision-making [3][4]. By evaluating users' affective response towards an information object (e.g. a document), prior and post to their exposure to it, a more accurate understanding of the object's properties and degree of relevance to the current information need may be facilitated. Furthermore, systems that can detect and respond accordingly to user emotions could potentially improve the naturalness of human-computer interaction and progressively optimize their retrieval strategy. The current study investigates the role of emotions in the information seeking process, as the latter are communicated through multi-modal interaction, and reconsiders relevance feedback with respect to what occurs on the affective level of interaction as well.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {891},
numpages = {1},
keywords = {affective interaction, relevance feedback, facial expression analysis},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390567,
author = {Liu, Chang},
title = {Exploring and Measuring Dependency Trees for Informationretrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390567},
doi = {10.1145/1390334.1390567},
abstract = {Natural language processing techniques are believed to hold a tremendous potential to supplement the purely quantitative methods of text information retrieval. This has led to the emergence of a large number of NLP-based IR research projects over the last few years, even though the empirical evidence to support this has often been inadequate. Most contributions of NLP to IR mainly concentrate on document representation and compound term matching strategies. Researchers have noted that the simple term-based representation of document content such as vector representation is usually inadequate for accurate discrimination. The "bag of words" representation does not invoke linguistic considerations and allow modelling of relationships between subsets of words. However, even though a variety of content indicator such as syntactic phrase have been tried and investigated for representing documents rather than single terms in IR systems, the matching strategy over those representation still cannot go beyond traditional statistical techniques that measure term co-occurrence characteristics and proximity in analyzing text structure.In this paper, we propose a novel IR strategy (SIR) with NLP techniques involved at the syntactic level. Within SIR, documents and query representation are built on the basis of a syntactic data structure of the natural language text - the dependency tree, in which syntactic relationships between words are identified and structured in the form of a tree. In order to capture the syntactic relations between words in their hierarchical structural representation, the matching strategy in SIR upgrades from the traditional statistical techniques by introducing a similarity measure method executing on the graph representation level as the key determiner. A basic IR experiment is designed and implemented on the TREC data to evaluate if this novel IR model is feasible. Experimental results indicate that this approach has the potential to outperform the standard bag of words IR model, especially in response to syntactical structured queries.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {892},
numpages = {1},
keywords = {tree matching, information retrieval, dependency tree},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390568,
author = {Serdyukov, Pavel},
title = {The Search for Expertise: To the Documents and Beyond},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390568},
doi = {10.1145/1390334.1390568},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {893},
numpages = {1},
keywords = {expert finding, enterprise search, expertise retrieval},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390569,
author = {Chernov, Sergey},
title = {Task Detection for Activity-Based Desktop Search},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390569},
doi = {10.1145/1390334.1390569},
abstract = {The desktop search tools provide powerful query capabilities and result presentation techniques. However, they do not take the user context into account. We propose to exploit collected information about user activities with desktop files and applications for activity-based desktop search. When I prepare for a project review and type in a search box the name of a colleague, I expect to find her last deliverable draft, but not her email with a paper review or our joint conference presentation. Ideally, the desktop search system should be able to infer my current task from the logs of my previous activities and present task-specific search results.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {894},
numpages = {1},
keywords = {machine learning, user studies, personal information management},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390570,
author = {Gan, Keng Hoon},
title = {Using a Mediated Query Approach for Matching Unstructured Query with Structured Resources},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390570},
doi = {10.1145/1390334.1390570},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {895},
numpages = {1},
keywords = {query, XML retrieval, search},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390571,
author = {Shah, Chirag},
title = {Understanding System Implementation and User Behavior in a Collaborative Information Seeking Environment},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390571},
doi = {10.1145/1390334.1390571},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {896},
numpages = {1},
keywords = {collaborative information seeking, user study, evaluation},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390572,
author = {Trieschnigg, Dolf},
title = {Biomedical Cross-Language Information Retrieval},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390572},
doi = {10.1145/1390334.1390572},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {897},
numpages = {1},
keywords = {language models, CLIR, biomedical IR},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390573,
author = {Meij, Edgar},
title = {Towards a Combined Model for Search and Navigation of Annotated Documents},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390573},
doi = {10.1145/1390334.1390573},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {898},
numpages = {1},
keywords = {semantic relatedness, language models, document annotations},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390574,
author = {Kelly, Liadh},
title = {Context and Linking in Retrieval from Personal Digital Archives},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390574},
doi = {10.1145/1390334.1390574},
abstract = {Advances in digital capture and storage technologies mean that it is now possible to capture and store one's entire life experiences in personal digital archives. These vast personal archives (or Human Digital Memories (HDMs)) pose new challenges and opportunities for the research community, not the least of which is developing effective means of retrieval from HDMs. Personal archive retrieval research is still in its infancy and there is much scope for novel research. My PhD proposes to develop effective HDM retrieval algorithms by combining rich sources of context associated with items, such as location and people present data, with information obtained by linking HDM items in novel ways.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {899},
numpages = {1},
keywords = {personal information management, context-based retrieval, human digital memories},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

@inproceedings{10.1145/1390334.1390575,
author = {Murray, G. Craig},
title = {Extending Language Modeling Techniques to Models of Search and Browsing Activity in a Digital Library},
year = {2008},
isbn = {9781605581644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1390334.1390575},
doi = {10.1145/1390334.1390575},
abstract = {Users searching for information in a digital library or on the WWW can be modeled as individuals moving through a semantic space by issuing queries and clicking on hyperlinks. As they go, they emit a stream of interaction data. Most of it is linguistic data. Lots of it is captured in logs. Some of it is used to guess what the user is searching for. But to most information retrieval systems, each user interaction is a stateless point in this space. There is a timeline connecting each of these points, but systems seldom make use of this as sequence data, in part because there is no clear way to systematically characterize the meaningful relations within a sequence of user activity. It is a problem of pragmatics as much as it is of semantics--the fact that a user clicked on a particular link, or added a particular term to their query, has meaning primarily in relation to the preceding actions. A remaining challenge in IR is to extract features of the user interaction data that will give meaning to those relations.Meanwhile, from the user's perspective each of these points in time and semantic space are just part of a path of exploration. To the user, the exact terms in a query, or the specific words surrounding a hypertext link, may be less important than the trajectory those terms establish in relation to the user's path. Identifying the meaningful relations between queries and page views within a sequence of activity increases our understanding of users and their information needs. Formally, we can model query and browsing behaviors as surface forms of a hidden process. What is missing is a layer of abstraction for mapping sequences of interaction in a way that is both descriptive of users' needs and useful to automation.The work I describe is an effort to identify features of data in logs of query and browsing activity that are highly predictive of certain types of behavior. Sequences of interaction data from individual users are modeled as sequences of expression. Statistical modeling techniques that are effective for modeling sequences in natural language processing and bioinformatics are examined for their ability to model sequences of interaction between an information searcher and an information retrieval system. Queries and click-throughs in this stream of interaction can be tagged with features such as semantic coordinates, timing, frequency of use, type of action, etc. By analyzing large collections of interaction sequences it is possible to identify frequent patterns of user behavior. From these patterns we can make predictions about future interactions. For example, certain patterns of link following in a digital library are highly predictive of users' next steps while other patterns are not.General models of user interaction are useful for design and evaluation of search interfaces. Individual models of user interaction are useful for personalized search and customized content. Yet very little research has been done to investigate which features are optimal for modeling user queries and browsing as interaction sequences. An important first step is to identify informative features and the relationships between features. I propose to construct models of user behavior based on user data in logs of query and browsing activity and to identify features that are highly predictive of certain types of user behaviors. I examine activity within search sessions on a digital library as a microcosm of larger systems. I expect to find features that are useful in predictive models of user behavior both at an individual and aggregate level. Where possible, I hope to identify meaningful relationships between those features. The work has implications beyond the scope of digital libraries, to larger systems and broader search domains.},
booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {900},
numpages = {1},
keywords = {sequence data, query modeling, predictive models, web logs, search behavior},
location = {Singapore, Singapore},
series = {SIGIR '08}
}

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr

branch_name: refs/heads/main

extension: bib


text:
require wpewebkit.inc
require conf/include/devupstream.inc

FILESEXTRAPATHS_prepend := "${THISDIR}/${PN}:"
SRC_URI = "\
    https://wpewebkit.org/releases/${BPN}-${PV}.tar.xz \
    file://216455_builds_with_ENABLE_SERVICE_WORKER_OFF.patch \
"

SRC_URI[sha256sum] = "4b577f4f07c34bb5d453d62b7a41d9e937bd3a2827a92dcd6f47c35f27c8359a"

DEPENDS += " libwpe"
RCONFLICTS_${PN} = "libwpe (< 1.4) wpebackend-fdo (< 1.6)"

SRC_URI_class-devupstream = "git://git.webkit.org/WebKit.git;branch=master"
SRCREV_class-devupstream = "8761d758f83fd7fda3d98a971cee02c38f765637"

license: MIT

repository: oceanicsystems/meta-webkit

license_type: permissive

repo_name: oceanicsystems/meta-webkit

branch_name: refs/heads/master

gha_license_id: MIT

gha_language: BitBake

extension: bb


text:
@article{Bradley2018,
 author = {Bradley, Brendon A. and Wotherspoon, Liam M. and Kaiser, Anna E. and Cox, Brady R. and Jeong, Seokho},
 doi = {10.1785/0120170286},
 issn = {0037-1106},
 journal = {Bulletin of the Seismological Society of America},
 month = {jul},
 number = {3B},
 pages = {1722--1735},
 title = {Influence of Site Effects on Observed Ground Motions in the Wellington Region from the Mw 7.8 Kaikōura, New Zealand, Earthquake},
 volume = {108},
 year = {2018}
}

repository: seokhojeong/academic-kickstart

repo_name: seokhojeong/academic-kickstart


text:
@article{johnston-peck_situ_2018,
 abstract = {Cerium dioxide nanocubes and truncated octahedra were reduced and oxidized in the scanning transmission electron microscope. The reduction process was stimulated by the electron beam and oxidation was supported by background gases in the microscope environment. High-angle annular dark ﬁeld imaging is sensitive to local lattice distortions that arise as oxygen vacancies are created and cerium cations reduce enabling high spatial resolution characterization of this process with temporal resolution on the order of seconds. Such measurements enable us to diﬀerentiate and infer that the observed behavior between the nanocubes and truncated octahedra may be due to the diﬀerence in crystallographic termination of surfaces. In situ measurements taken with different partial pressures of oxygen reveal the cerium oxidation state and the dose rate threshold for the onset of beam reduction are inﬂuenced by the environment. Increasing oxygen partial pressure reduces the Ce3+ content and decreases susceptibility to electron beam driven reduction.},
 author = {Johnston-Peck, Aaron C. and Yang, Wei-Chang D. and Winterstein, Jonathan P. and Sharma, Renu and Herzing, Andrew A.},
 copyright = {All rights reserved},
 doi = {10.1016/j.micron.2018.08.008},
 file = {Johnston-Peck et al. - 2018 - In situ oxidation and reduction of cerium dioxide .pdf:C\:\\Users\\wdy\\Zotero\\storage\\I93IKAAS\\Johnston-Peck et al. - 2018 - In situ oxidation and reduction of cerium dioxide .pdf:application/pdf},
 issn = {09684328},
 journal = {Micron},
 language = {en},
 month = {December},
 pages = {54--63},
 title = {In situ oxidation and reduction of cerium dioxide nanoparticles studied by scanning transmission electron microscopy},
 url = {https://linkinghub.elsevier.com/retrieve/pii/S0968432818302361},
 urldate = {2020-01-02},
 volume = {115},
 year = {2018}
}

repository: wcdyang/academic-kickstart

repo_name: wcdyang/academic-kickstart


text:
DESCRIPTION = "A couple of useful Lua modules"
LICENSE = "MIT"
RDEPENDS:${PN} = "lua"
LIC_FILES_CHKSUM = "file://License;md5=596f176fbcaaafa9489a583a69acba69"

PV = "1.1.2"
PR = "r2"
BRANCH = "master"
TAG = "30864a67e6f49f30b5110694de895af56b2975b7"

SRC_URI = "git://github.com/kmarkus/uutils.git;protocol=https;branch=${BRANCH};tag=${TAG}"

S = "${WORKDIR}/git"

LUA_MODULE_DIR = "/usr/share/lua"
FILES:${PN} += "\
	${LUA_MODULE_DIR}/5.1/strict.lua\
	${LUA_MODULE_DIR}/5.1/ansicolors.lua\
	${LUA_MODULE_DIR}/5.1/utils.lua\
	${LUA_MODULE_DIR}/5.1/time.lua\
	${LUA_MODULE_DIR}/5.2/strict.lua\
	${LUA_MODULE_DIR}/5.2/ansicolors.lua\
	${LUA_MODULE_DIR}/5.2/utils.lua\
	${LUA_MODULE_DIR}/5.2/time.lua\
	${LUA_MODULE_DIR}/5.3/strict.lua\
	${LUA_MODULE_DIR}/5.3/ansicolors.lua\
	${LUA_MODULE_DIR}/5.3/utils.lua\
	${LUA_MODULE_DIR}/5.3/time.lua \
	${LUA_MODULE_DIR}/5.4/strict.lua \
	${LUA_MODULE_DIR}/5.4/ansicolors.lua\
	${LUA_MODULE_DIR}/5.4/utils.lua \
	${LUA_MODULE_DIR}/5.4/time.lua"

do_install () {
oe_runmake 'DESTDIR=${D}' install
}

repository: kmarkus/meta-microblx

repo_name: kmarkus/meta-microblx


text:
---
---

@inproceedings{puglisi2023deepbrainprint,
   title={DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification},
   author={Puglisi, Lemuel and Eshaghi, Arman and Parker, Geoff and Barkhof, Frederik and Alexander, Daniel C and Ravi, Daniele},
   abstract={Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is designed to retrieve brain MRI scans of the same patient. Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations. First, we use a combination of self-supervised and supervised paradigms to create an effective brain fingerprint from MRI scans that can be used for real-time image retrieval. Second, we use a special weighting function to guide the training and improve model convergence. Third, we introduce new imaging transformations to improve retrieval robustness in the presence of intensity variations (i.e. different scan contrasts), and to account for age and disease progression in patients. We tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset designed to evaluate retrieval performance with different image modalities. Our results show that DeepBrainPrint outperforms previous methods, including simple similarity metrics and more advanced contrastive deep learning frameworks.},
   booktitle={Medical Imaging with Deep Learning,},
   year={2023},

   abbr={MIDL2023},
   code={https://github.com/DeepBrainPrint/DeepBrainPrint},
   pdf={deepbrainprint-article.pdf},
   poster={deepbrainprint-poster.pdf},
   selected={true},
}


text:
@inproceedings{andersen_using_2014,
	title = {Using robot skills for flexible reprogramming of pick operations in industrial scenarios},
	url = {http://tapas.informatik.uni-freiburg.de/files/rasmus14visapp.pdf},
	urldate = {2015-08-01},
	booktitle = {International {Joint} {Conference} on {Computer} {Vision}, {Imaging} and {Computer} {Graphics} {Theory} and {Applications}},
	author = {Andersen, Rasmus S. and Nalpantidis, Lazaros and Krüger, Volker and Madsen, Ole and Moeslund, Thomas B.},
	year = {2014},
	pages = {678--685},
	file = {rasmus14visapp.pdf:/home/deebuls/.mozilla/firefox/csciq0z7.default/zotero/storage/ISQU9NDP/rasmus14visapp.pdf:application/pdf}
}

@article{kruger_meaning_2007,
	title = {The {Meaning} of {Action} {A} review on action recognition and mapping {Report} number: {CVMI} 2007: 1 {ISSN} 1902-2034},
	shorttitle = {The {Meaning} of {Action} {A} review on action recognition and mapping {Report} number},
	author = {Krüger, Volker and Kragic, Danica and Ude, Aleš and Geib, Christopher},
	year = {2007},
	file = {action08.pdf:/home/deebuls/.mozilla/firefox/csciq0z7.default/zotero/storage/VU2TWNRD/action08.pdf:application/pdf}
}

@article{pedersen_robot_2015,
	title = {Robot skills for manufacturing: {From} concept to industrial deployment},
	issn = {07365845},
	shorttitle = {Robot skills for manufacturing},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0736584515000575},
	doi = {10.1016/j.rcim.2015.04.002},
	language = {en},
	urldate = {2015-05-21},
	journal = {Robotics and Computer-Integrated Manufacturing},
	author = {Pedersen, Mikkel Rath and Nalpantidis, Lazaros and Andersen, Rasmus Skovgaard and Schou, Casper and Bøgh, Simon and Krüger, Volker and Madsen, Ole},
	month = apr,
	year = {2015},
	file = {1-s2.0-S0736584515000575-main.pdf:/home/deebuls/.mozilla/firefox/csciq0z7.default/zotero/storage/BFTGI2NR/1-s2.0-S0736584515000575-main.pdf:application/pdf}
}

@article{rovida_project_2014,
	title = {Project no.: 610917 {Project} full title: {Sustainable} and reliable robotics for part handling in manufacturing {Project} {Acronym}: {STAMINA} {Deliverable} no.: {D}3.},
	shorttitle = {Project no.},
	url = {http://stamina-robot.eu/upload/dokumenter/Deliverables/STAMINA_D3.1_v1.0.pdf},
	urldate = {2015-05-21},
	author = {Rovida, Francesco},
	year = {2014},
	file = {STAMINA_D3.1_v1.0.pdf:/home/deebuls/.mozilla/firefox/csciq0z7.default/zotero/storage/DNM84RH8/STAMINA_D3.1_v1.0.pdf:application/pdf}
}

@inproceedings{pedersen_integration_2013,
	title = {On the integration of hardware-abstracted robot skills for use in industrial scenarios},
	url = {http://www.tapasproject.eu/files/pedersen13wscrs.pdf},
	urldate = {2015-05-16},
	booktitle = {2nd {International} {Workshop} on {Cognitive} {Robotics} {Systems}: {Replicating} {Human} {Actions} and {Activities}},
	author = {Pedersen, Mikkel Rath and Nalpantidis, Lazaros and Bobick, Aaron and Krüger, Volker},
	year = {2013},
	pages = {1166--1171},
	file = {pedersen13wscrs.pdf:/home/deebuls/.mozilla/firefox/csciq0z7.default/zotero/storage/5PGMFWI7/pedersen13wscrs.pdf:application/pdf}
}

@article{kruger_learning_2010,
	title = {Learning {Actions} from {Observations}},
	volume = {17},
	issn = {1070-9932},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5481141},
	doi = {10.1109/MRA.2010.936961},
	number = {2},
	urldate = {2015-05-16},
	journal = {IEEE Robotics \& Automation Magazine},
	author = {Kruger, Volker and Herzog, Dennis and Baby, Sanmohan and Ude, Aleš and Kragic, Danica},
	month = jun,
	year = {2010},
	pages = {30--43},
	file = {05481141.pdf:/home/deebuls/.mozilla/firefox/csciq0z7.default/zotero/storage/ZQTDNBQP/05481141.pdf:application/pdf}
}

@article{bogh_identifying_2012,
	title = {Identifying and evaluating suitable tasks for autonomous industrial mobile manipulators ({AIMM})},
	volume = {61},
	issn = {0268-3768, 1433-3015},
	url = {http://link.springer.com/10.1007/s00170-011-3718-3},
	doi = {10.1007/s00170-011-3718-3},
	language = {en},
	number = {5-8},
	urldate = {2015-05-16},
	journal = {The International Journal of Advanced Manufacturing Technology},
	author = {Bøgh, Simon and Hvilshøj, Mads and Kristiansen, Morten and Madsen, Ole},
	month = jul,
	year = {2012},
	pages = {713--726},
	file = {art%3A10.1007%2Fs00170-011-3718-3.pdf:/home/deebuls/.mozilla/firefox/csciq0z7.default/zotero/storage/H8DSSAH8/art%3A10.1007%2Fs00170-011-3718-3.pdf:application/pdf}
}

@inproceedings{bogh_does_2012,
	title = {Does your robot have skills?},
	url = {http://forskningsbasen.deff.dk/Share.external?sp=S9f184d42-459a-4d30-871c-bab8082cfbba&sp=Saau},
	urldate = {2015-05-16},
	booktitle = {The 43rd {Intl}. {Symp}. on {Robotics} ({ISR}2012)},
	author = {Bøgh, Simon and Nielsen, Oluf Skov and Pedersen, Mikkel Rath and Krüger, Volker and Madsen, Ole},
	year = {2012},
	file = {ISR_2012_Paper_ID_1158_FINAL.pdf:/home/deebuls/.mozilla/firefox/csciq0z7.default/zotero/storage/H8PPDDG3/ISR_2012_Paper_ID_1158_FINAL.pdf:application/pdf}
}

repository: deebuls/RecommenderSystemInRobotics

repo_name: deebuls/RecommenderSystemInRobotics