ABSTRACT
The focused retrieval task is to rank documents' passages by their presumed relevance to a query. Inspired by work on cluster-based document retrieval, we present a novel cluster-based focused retrieval method. The method is based on ranking clusters of similar passages using a learning-to-rank approach and transforming the cluster ranking to passage ranking. Empirical evaluation demonstrates the clear merits of the method.
- Paavo Arvola, Shlomo Geva, Jaap Kamps, Ralf Schenkel, Andrew Trotman, and Johanna Vainio. 2011. Overview of the INEX 2010 ad hoc track. In Comparative Evaluation of Focused Retrieval. 1--32.Google Scholar
- Michael Bendersky, W Bruce Croft, and Yanlei Diao. 2011. Quality-biased ranking of web documents. In Proc. of WSDM. 95--104.Google ScholarDigital Library
- David Buffoni, Nicolas Usunier, and Patrick Gallinari. 2010. Lip6 at INEX: OWPC for ad hoc track. In Focused Retrieval and Evaluation . 59--69.Google Scholar
- James P. Callan. 1994. Passage-Level Evidence in Document Retrieval. In Proc. of SIGIR. 302--301.Google ScholarDigital Library
- Ruey-Cheng Chen, Evi Yulianti, Mark Sanderson, and W Bruce Croft. 2017. On the Benefit of Incorporating External Features in a Neural Architecture for Answer Sentence Selection. In Proc. of SIGIR. 1017--1020.Google ScholarDigital Library
- Daniel Cohen and W Bruce Croft. 2016. End to end long short term memory networks for non-factoid question answering. In Proc. of ICTIR. 143--146.Google ScholarDigital Library
- Shlomo Geva, Jaap Kamps, Miro Lethonen, Ralf Schenkel, James A Thom, and Andrew Trotman. 2010. Overview of the INEX 2009 ad hoc track. In Focused retrieval and evaluation . 4--25.Google Scholar
- Nick Jardine and C. J. van Rijsbergen. 1971. The use of hierarchic clustering in information retrieval. Information storage and retrieval , Vol. 7, 5 (1971), 217--240.Google Scholar
- Thorsten Joachims. 2006. Training linear SVMs in linear time. In Proc. of KDD. 217--226.Google ScholarDigital Library
- Oren Kurland. 2009. Re-ranking search results using language models of query-specific clusters. Information Retrieval , Vol. 12, 4 (2009), 437--460.Google ScholarDigital Library
- Oren Kurland and Carmel Domshlak. 2008. A rank-aggregation approach to searching for optimal query-specific clusters. In Proc. of SIGIR. 547--554.Google ScholarDigital Library
- Oren Kurland and Eyal Krikon. 2011. The opposite of smoothing: a language model approach to ranking query-specific document clusters. Journal of Artificial Intelligence Research , Vol. 41 (2011), 367--395.Google ScholarDigital Library
- Oren Kurland and Lillian Lee. 2004. Corpus structure, language models, and ad hoc information retrieval. In Proc. of SIGIR . 194--201.Google ScholarDigital Library
- Xiaoyong Liu and W Bruce Croft. 2004. Cluster-based retrieval using language models. In Proc. of SIGIR. 186--193.Google ScholarDigital Library
- Xiaoyong Liu and W Bruce Croft. 2006. Experiments on retrieval of optimal clusters . Technical Report. Technical Report IR-478, Center for Intelligent Information Retrieval (CIIR), University of Massachusetts.Google Scholar
- Xiaoyong Liu and W Bruce Croft. 2008. Evaluating text representations for retrieval of the best group of documents. In Proc. of ECIR . 454--462.Google ScholarCross Ref
- Vanessa Graham Murdock. 2006. Aspects of sentence retrieval . Ph.D. Dissertation. University of Massachusetts Amherst.Google Scholar
- Fiana Raiber and Oren Kurland. 2013. Ranking document clusters using markov random fields. In Proc. of SIGIR . 333--342.Google ScholarDigital Library
- Tetsuya Sakai, Toshihiko Manabe, and Makoto Koyama. 2005. Flexible pseudo-relevance feedback via selective sampling. TALIP , Vol. 4, 2 (2005), 111--135.Google ScholarDigital Library
- Gerard Salton, James Allan, and Chris Buckley. 1993. Approaches to passage retrieval in full text information systems. In Proc. of SIGIR . 49--58.Google ScholarDigital Library
- Aliaksei Severyn and Alessandro Moschitti. 2015. Learning to rank short text pairs with convolutional deep neural networks. In Proc. of SIGIR. 373--382.Google ScholarDigital Library
- Eilon Sheetrit, Anna Shtok, Oren Kurland, and Igal Shprincis. 2018. Testing the Cluster Hypothesis with Focused and Graded Relevance Judgments. In Proc. of SIGIR . 1173--1176.Google ScholarDigital Library
- Ian Soboroff. 2004. Overview of the TREC 2004 Novelty Track. In Proc. of TREC .Google Scholar
- Ian Soboroff and Donna Harman. 2003. Overview of the TREC 2003 Novelty Track. In Proc. of TREC. 38--53.Google Scholar
- Anastasios Tombros, Robert Villa, and C. J. Van Rijsbergen. 2002. The effectiveness of query-specific hierarchic clustering in information retrieval. Information processing & management , Vol. 38, 4 (2002), 559--582.Google Scholar
- Ellen M. Voorhees. 1985. The cluster hypothesis revisited. In Proc. of SIGIR. 188--196.Google ScholarDigital Library
- Liu Yang, Qingyao Ai, Jiafeng Guo, and W Bruce Croft. 2016a. aNMM: Ranking short answer texts with attention-based neural matching model. In Proc. of CIKM. 287--296.Google ScholarDigital Library
- Liu Yang, Qingyao Ai, Damiano Spina, Ruey-Cheng Chen, Liang Pang, W Bruce Croft, Jiafeng Guo, and Falk Scholer. 2016b. Beyond factoid QA: Effective methods for non-factoid answer sentence retrieval. In Proc. of ECIR. 115--128.Google ScholarCross Ref
- Chengxiang Zhai and John Lafferty. 2001. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proc. of SIGIR. 334--342.Google ScholarDigital Library
Index Terms
- Cluster-Based Focused Retrieval
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