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Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction

Summary

Web clustering engines group search results by surface word overlap. As a result, snippets that share a meaning but no vocabulary end up apart, and snippets that share words but differ in meaning get merged. Di Marco and Navigli address this for ambiguous queries using Word Sense Induction (WSI). The query's meanings are first discovered automatically from a large raw corpus (Web1T or ukWaC) as clusters of words that co-occur with it in a graph. Each returned snippet is then assigned to the sense it is semantically closest to. The authors build an end-to-end framework that judges induced senses by the quality and diversity of the resulting result clusters. They compare several graph-based WSI algorithms, including two new ones, SquaT++ and B-MST, and add a manual check of sense quality. The semantic approach beats non-semantic clustering systems (Lingo, STC, KeySRC) and plain search-engine rankings. Because the method needs only a large corpus and basic text tools, it is language-independent and could be applied to Ukrainian.

Key points

  • Algorithms compared: Curvature, SquaT++ (V/E variants), B-MST, HyperLex and Chinese Whispers. Of these, B-MST, HyperLex and Chinese Whispers performed best and are also the cheapest to run.
  • Speed: HyperLex needs roughly 0.1–0.16 s per query, which is comparable to Lingo and STC. SquaT++ E can take about 169 s per query on ukWaC.
  • New resource: MORESQUE, a dataset of 114 ambiguous queries with 11,400 sense-annotated snippets (lcl.uniroma1.it/moresque).
  • The framework became the basis for SemEval-2013 Task 11, which evaluates WSI extrinsically.
  • Adapting the method to another language calls for a corpus from which to extract co-occurrences, plus a list of stopwords, a lemmatization tool and a tool for identifying compounds.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing · Computing & GPUs

Topics: Word sense disambiguation · Information retrieval & search

Institutions

🎓Sapienza University of RomeRome · Italy29 works · 6 807 citations

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00148

@article{marco2013clustering,
  title = {{Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction}},
  author = {Marco, Antonio Di and Navigli, Roberto},
  journal = {Computational Linguistics},
  year = {2013},
  doi = {10.1162/coli\_a\_00148},
  url = {https://doi.org/10.1162/coli_a_00148}
}

References

106 references, 10 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Agirre, Eneko, David Martı́nez, Oier López de Lacalle, and Aitor Soroa. 2006a. Evaluating and optimizing the parameters of an unsupervised graph-based WSD algorithm. In Proceedings of the 1st Workshop on Graph-Based Algorithms for Natural Language Processing, pages 89–96, New York. find
  2. Agirre, Eneko, David Martı́nez, Oier López de Lacalle, and Aitor Soroa. 2006b. Two graph-based algorithms for state-of-the-art WSD. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 585–593, Sydney. find
  3. Agirre, Eneko and Aitor Soroa. 2007. UBC-AS: A graph based unsupervised system for induction and classification. In Proceedings of the 4 th International Workshop on Semantic Evaluations, pages 346–349, Prague. find
  4. Agrawal, Rakesh, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. 2009. Diversifying search results. In Proceedings of the 2nd International Conference on Web Search and Web Data Mining, pages 5–14, Barcelona. find
  5. Baroni, Marco and Alessandro Lenci. 2010. Distributional memory: A general framework for corpus-based semantics. Computational Linguistics, 36(4):673–721. find our card
  6. Basile, Pierpaolo, Annalina Caputo, and Giovanni Semeraro. 2009. Exploiting disambiguation and discrimination in Information Retrieval systems. In Proceedings of the 2009 IEEE/WIC/ ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03, pages 539–542, Was… find
  7. Bennett, Paul N. and Nam Nguyen. 2009. Refined experts: Improving classification in large taxonomies. In Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 11–18, Boston, MA. find
  8. Bernardini, Andrea, Claudio Carpineto, and Massimiliano D’Amico. 2009. Full-subtopic retrieval with keyphrase-based search results clustering. In Proceedings of 2009 IEEE/WIC/ACM International Conference on Web Intelligence, pages 206–213, Milan. find
  9. Biemann, Chris. 2006. Chinese whispers—an efficient graph clustering algorithm and its application to Natural Language Processing problems. In Proceedings of the 1st Workshop on Graph-Based Algorithms for Natural Language Processing, pages 73–80, New York. find
  10. Boleda, Gemma, Sabine Schulte im Walde, and Toni Badia. 2012. Modeling regular polysemy: A study on the semantic classification of Catalan adjectives. Computational Linguistics, 38(3):575–616. find
  11. Branson, S. and A. Greenberg. 2002. Clustering Web search results using suffix tree methods. In Technical Report CS276A Final Project, Stanford University. find
  12. Brants, Thorsten and Alex Franz. 2006. Web 1T 5-gram, ver. 1, ldc2006t13. In Linguistic Data Consortium, Philadelphia, PA. find
  13. Brody, Samuel and Mirella Lapata. 2009. Bayesian Word Sense Induction. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pages 103–111, Athens. find our card
  14. Bruza, Peter, Robert McArthur, and Simon Dennis. 2000. Interactive Internet search: Keyword, directory and query reformulation mechanisms compared. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 280–287, Athens. find
  15. Bunescu, Razvan C. and Marius Pasca. 2006. Using encyclopedic knowledge for named entity disambiguation. In Proceedings of 11th Conference of the European Chapter of the Association for Computational Linguistics, pages 9–16, Trento. find our card
  16. Carbonell, Jaime and Jade Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 335–336, Melbourne. find
  17. Carmel, David, Haggai Roitman, and Naama Zwerdling. 2009. Enhancing cluster labeling using Wikipedia. In Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 139–146, Boston, MA. find
  18. Carpineto, Claudio, Massimiliano D’Amico, and Andrea Bernardini. 2011. Full discrimination of subtopics in search results with keyphrase-based clustering. Web Intelligence and Agent Systems, 9(4):337–349. find
  19. Carpineto, Claudio, Stanislaw Osiński, Giovanni Romano, and Dawid Weiss. 2009. A survey of Web clustering engines. ACM Computing Surveys, 41(3):1–38. find
  20. Carpineto, Claudio and Giovanni Romano. 2004. Exploiting the potential of concept lattices for Information Retrieval with CREDO. Journal of Universal Computer Science, 10(8):985–1013. find
  21. Chandar, Praveen and Ben Carterette. 2010. Diversification of search results using webgraphs. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 869–870, Geneva. find
  22. Chapelle, Olivier, Yi Chang, and Tie-Yan Liu. 2011. Future directions in learning to rank. Journal of Machine Learning Research—Proceedings Track, 14:91–100. find
  23. Chen, Harr and David R. Karger. 2006. Less is more: Probabilistic models for retrieving fewer relevant documents. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 429–436, Seattle, WA. find
  24. Chen, Jiyang, Osmar R. Zaı̈ane, and Randy Goebel. 2008. An unsupervised approach to cluster web search results based on word sense communities. In Proceedings of 2008 IEEE/WIC/ACM International Conference on Web Intelligence, pages 725–729, Sydney. find
  25. Cheng, David, Santosh Vempala, Ravi Kannan, and Grant Wang. 2005. A divide-and-merge methodology for clustering. In Proceedings of the 24th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 196–205, Baltimore, MD. find
  26. Clarke, Daoud. 2012. A context-theoretic framework for compositionality in distributional semantics. Computational Linguistics, 38(1):41–71. find our card
  27. Clough, Paul, Mark Sanderson, Murad Abouammoh, Sergio Navarro, and Monica Lestari Paramita. 2009. Multiple approaches to analysing query diversity. In Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 734–735, Boston, MA. find
  28. Crabtree, Daniel, Xiaoying Gao, and Peter Andreae. 2005. Improving Web clustering by cluster selection. In Proceedings of 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pages 172–178, Compiegne. find
  29. Cutting, Douglass R., David R. Karger, Jan O. Pedersen, and John W. Tukey. 1992. Scatter/Gather: A cluster-based approach to browsing large document collections. In Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 318–329,… find
  30. Di Giacomo, Emilio, Walter Didimo, Luca Grilli, and Giuseppe Liotta. 2007. Graph visualization techniques for Web clustering engines. IEEE Transactions on Visualization and Computer Graphics, 13(2):294–304. find
  31. Di Marco, Antonio and Roberto Navigli. 2011. Clustering Web search results with maximum spanning trees. In Proceedings of the XIIth International Conference of the Italian Association for Artificial Intelligence, pages 201–212, Palermo. find
  32. Dorow, Beate, Dominic Widdows, Katarina Ling, Jean-Pierre Eckmann, Danilo Sergi, and Elisha Moses. 2005. Using curvature and Markov clustering in graphs for lexical acquisition and word sense discrimination. In Proceedings of the Meaning-2005 Workshop, Trento. find
  33. Erk, Katrin, Sebastian Padó, and Ulrike Padó. 2010. A flexible, corpus-driven model of regular and inverse selectional preferences. Computational Linguistics, 36(4):723–763. find
  34. Fellbaum, Christiane, editor. 1998. WordNet: An Electronic Database. MIT Press, Cambridge, MA. find
  35. Ferraresi, Adriano, Eros Zanchetta, Marco Baroni, and Silvia Bernardini. 2008. Introducing and evaluating ukWaC, a very large Web-derived corpus of English. In Proceedings of the 4th Web as Corpus Workshop (WAC-4), pages 47–54, Marrakech. find
  36. Furnas, G. W., T. K. Landauer, L. M. Gomez, and S. T. Dumais. 1987. The vocabulary problem in human-system communication. Commununications of ACM, 30(11):964–971. find
  37. Gabrilovich, Evgeniy and Shaul Markovitch. 2009. Wikipedia-based semantic interpretation for natural language processing. Journal of Artificial Intelligence Research (JAIR), 34:443–498. find
  38. Geiss, Johanna. 2009. Creating a gold standard for sentence clustering in multi-document summarization. In Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pages 96–104, Sin… find
  39. Gelgi, Fatih, Hasan Davulcu, and Srinivas Vadrevu. 2007. Term ranking for clustering Web search results. In Proceedings of 10th International Workshop on the Web and Databases, Beijing, China. find
  40. Gonzalo, Julio, Anselmo Penas, and Felisa Verdejo. 1999. Lexical ambiguity and Information Retrieval revisited. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pages 195–202, College Park, MD. find
  41. Harris, Zellig. 1954. Distributional structure. Word, 10:146–162. find
  42. Hubert, L. and P. Arabie. 1985. Comparing partitions. Journal of Classification, 2(1):193–218. find
  43. Kamvar, Maryam and Shumeet Baluja. 2006. A large scale study of wireless search behavior: Google mobile search. In Proceedings of the 2006 Conference on Human Factors in Computing Systems, pages 701–709, Montréal. find
  44. Ke, Weimao, Cassidy R. Sugimoto, and Javed Mostafa. 2009. Dynamicity vs. effectiveness: Studying online clustering for Scatter/Gather. In Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 19–26, Boston, MA. find
  45. Kim, Sang-Bum, Hee-Cheol Seo, and Hae-Chang Rim. 2004. Information Retrieval using word senses: Root sense tagging approach. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 258–265, Sheffield. find
  46. Korkontzelos, Ioannis and Suresh Manandhar. 2010. UoY: Graphs of unambiguous vertices for word sense induction and disambiguation. In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 355–358, Uppsala. find
  47. Krovetz, Robert and William B. Croft. 1992. Lexical ambiguity and Information Retrieval. ACM Transactions on Information Systems, 10(2):115–141. find
  48. Kurland, Oren. 2008. The opposite of smoothing: A language model approach to ranking query-specific document clusters. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 171–178, Singapore. find
  49. Kurland, Oren and Carmel Domshlak. 2008. A rank-aggregation approach to searching for optimal query-specific clusters. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 547–554, Singapore. find
  50. Lee, Kyung Soon, W. Bruce Croft, and James Allan. 2008. A cluster-based resampling method for pseudo-relevance feedback. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 235–242, Singapore. find
  51. Lin, Dekang. 1998. Automatic retrieval and clustering of similar words. In Proceedings of the 17 th International Conference on Computational Linguistics, pages 768–774, Montreal. find our card
  52. Lin, Dekang and Patrick Pantel. 2002. Discovering word senses from text. In Proceedings of the 8 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 613–619, Edmonton. find
  53. Liu, Shuang, Clement Yu, and Weiyi Meng. 2005. Word Sense Disambiguation in queries. In Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management, pages 525–532, Bremen. find
  54. Liu, Tie-Yan, Yiming Yang, Hao Wan, Hua-Jun Zeng, Zheng Chen, and Wei-Ying Ma. 2005. Support vector machines classification with a very large-scale taxonomy. SIGKDD Explorations, 7(1):36–43. find
  55. Liu, Ying, Wenyuan Li, Yongjing Lin, and Liping Jing. 2008. Spectral geometry for simultaneously clustering and ranking query search results. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 539–546, Singapore. find
  56. Ma, Hao, Michael R. Lyu, and Irwin King. 2010. Diversifying query suggestion results. In Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010, pages 1,399–1,404, Atlanta, GA. find
  57. Maarek, Yoelle, Ron Fagin, Israel Ben-Shaul, and Dan Pelleg. 2000. Ephemeral document clustering for Web applications. IBM Research Report RJ 10186. Haifa. find
  58. Manandhar, Suresh, Ioannis P. Klapaftis, Dmitriy Dligach, and Sameer S. Pradhan. 2010. SemEval-2010 task 14: Word sense induction & disambiguation. In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 63–68, Uppsala. find our card
  59. Mandala, Rila, Takenobu Tokunaga, and Hozumi Tanaka. 1998. The use of WordNet in Information Retrieval. In Proceedings of the COLING-ACL workshop on Usage of Wordnet in Natural Language Processing, pages 31–37, Montréal. find
  60. Matsuo, Yutaka, Takeshi Sakaki, Kôki Uchiyama, and Mitsuru Ishizuka. 2006. Graph-based word clustering using a Web search engine. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 542–550, Sydney. find
  61. Mihalcea, Rada. 2007. Using Wikipedia for automatic Word Sense Disambiguation. find
  62. In Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, pages 196–203, Rochester, NY. find
  63. Mihalkova, L. and R. Mooney. 2009. Learning to disambiguate search queries from short sessions. In Proceedings of Machine Learning and Knowledge Discovery in Databases (2), pages 111–127, Bled. find
  64. Miller, George A., R. T. Beckwith, Christiane D. Fellbaum, D. Gross, and K. Miller. 1990. WordNet: an online lexical database. International Journal of Lexicography, 3(4):235–244. find
  65. Mitchell, Jeff and Mirella Lapata. 2010. Composition in distributional models of semantics. Cognitive Science, 34(8):1388–1429. find
  66. Navigli, Roberto. 2009. Word Sense Disambiguation: A survey. ACM Computing Surveys, 41(2):1–69. find
  67. Navigli, Roberto. 2012. A quick tour of Word Sense Disambiguation, induction and related approaches. In Proceedings of the 38th Conference on Current Trends in Theory and Practice of Computer Science, pages 115–129, Spindleruv Mlýn. find
  68. Navigli, Roberto and Giuseppe Crisafulli. 2010. Inducing word senses to improve Web search result clustering. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 116–126, Boston, MA. find
  69. Navigli, Roberto and Simone Paolo Ponzetto. 2012. The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence, 193:217–250. find
  70. Ngo, Chi Lang and Hung Son Nguyen. 2005. A method of Web search result clustering based on rough sets. In Proceedings of 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pages 673–679, Compiegne. find
  71. Nguyen, Cam-Tu, Xuan-Hieu Phan, Susumu Horiguchi, Thu-Trang Nguyen, and Quang-Thuy Ha. 2009. Web search clustering and labeling with hidden topics. ACM Transactions on Asian Language Information Processing, 8(3):1–40. find
  72. Osinski, Stanislaw and Dawid Weiss. 2005. A concept-driven algorithm for clustering search results. IEEE Intelligent Systems, 20(3):48–54. find
  73. Rand, W. M. 1971. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336):846–850. find
  74. Reisinger, Joseph and Marius Pasca. 2011. Fine-grained class label markup of search queries. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1,200–1,209, Portland, OR. find
  75. Rosenberg, Andrew and Julia Hirschberg. 2007. V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 410–420, Prague. find our card
  76. Sanderson, Mark. 1994. Word Sense Disambiguation and Information Retrieval. In Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, pages 142–151, Dublin. find
  77. Sanderson, Mark. 2000. Retrieving with good sense. Information Retrieval, 2(1):49–69. find
  78. Sanderson, Mark. 2008. Ambiguous queries: Test collections need more sense. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 499–506, Singapore. find
  79. Santamarı́a, Celina, Julio Gonzalo, and Javier Artiles. 2010. Wikipedia as sense inventory to improve diversity in Web search results. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010, pages 1,357–1,366, Uppsala. find
  80. Schütze, Hinrich. 1992. Dimensions of meaning. In Proceedings of the 1992 ACM/IEEE Conference on Supercomputing, pages 787–796, Los Alamitos, CA. find
  81. Schütze, Hinrich. 1998. Automatic word sense discrimination. Computational Linguistics, 24(1):97–124. find our card
  82. Schütze, Hinrich and Jan Pedersen. 1995. Information Retrieval based on word senses. In Proceedings of SDAIR’95, pages 161–175, Las Vegas, NV. find
  83. Silberer, Carina and Mirella Lapata. 2012. Grounded models of semantic representation. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1,423–1,433, Jeju Island. find
  84. Smadja, Frank, Kathleen R. McKeown, and Vasileios Hatzivassiloglou. 1996. Translating collocations for bilingual lexicons: A statistical approach. Computational Linguistics, 22(1):1–38. find our card
  85. Song, Ruihua, Zhenxiao Luo, Jian-Yun Nie, Yong Yu, and Hsiao-Wuen Hon. 2009. Identification of ambiguous queries in Web search. Information Processing and Management, 45:216–229. find
  86. Steinley, Doug. 2004. Properties of the Hubert-Arabie adjusted Rand index. Psychological Methods, 9(3):386–396. find
  87. Stokoe, Christopher, Michael J. Oakes, and John I. Tait. 2003. Word Sense Disambiguation in Information Retrieval revisited. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 159–166, Toronto. find
  88. Swaminathan, Ashwin, Cherian V. Mathew, and Darko Kirovski. 2009. Essential pages. In Proceedings of 2009 IEEE/WIC/ACM International Conference on Web Intelligence, pages 173–182, Milan. find
  89. Udani, Goldee, Shachi Dave, Anthony Davis, and Tim Sibley. 2005. Noun sense induction using Web search results. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 657–658, Salvador. find
  90. Van de Cruys, Tim and Marianna Apidianaki. 2011. Latent semantic word sense induction and disambiguation. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1,476–1,485, Portland, OR. van Rijsbergen, C. J. 1979. Information… find
  91. Véronis, Jean. 2004. HyperLex: Lexical cartography for information retrieval. Computer, Speech and Language, 18(3):223–252. von Luxburg, Ulrike, Robert C. Williamson, and Isabelle Guyon. 2012. Clustering: Science or art? Journal of Machine Learning Research—Proceedings Track, 27:65–80. find
  92. Voorhees, Ellen M. 1993. Using WordNet to disambiguate word senses for text retrieval. In Proceedings of the 16th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, pages 171–180, Pittsburgh, PA. find
  93. Wang, Xuanhui, Deepayan Chakrabarti, and Kunal Punera. 2009. Mining broad latent query aspects from search sessions. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 867–876, Paris. find
  94. Wang, Xuanhui and ChengXiang Zhai. 2007. Learn from Web search logs to organize search results. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 87–94, Amsterdam. find
  95. Widdows, Dominic and Beate Dorow. 2002. A graph model for unsupervised lexical acquisition. In Proceedings of the 19th International Conference on Computational Linguistics, pages 1–7, Taipei. find
  96. Wu, Fei, Jayant Madhavan, and Alon Y. Halevy. 2011. Identifying aspects for Web-search queries. Journal of Artificial Intelligence Research (JAIR), 40:677–700. find
  97. Xue, Gui-Rong, Dikan Xing, Qiang Yang, and Yong Yu. 2008. Deep classification in large-scale text hierarchies. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 619–626, Singapore. find
  98. Xue, Xiaobing and Xiaoxin Yin. 2011. Topic modeling for named entity queries. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pages 2009–2012, New York. find
  99. Yarowsky, David. 1993. One sense per collocation. In Proceedings of the ARPA Workshop on Human Language Technology, pages 266–271, Princeton, NJ. find our card
  100. Zamir, Oren and Oren Etzioni. 1998. Web document clustering: A feasibility demonstration. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 46–54, Melbourne. find
  101. Zamir, Oren, Oren Etzioni, Omid Madani, and Richard M. Karp. 1997. Fast and intuitive clustering of Web documents. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pages 287–290, Newport Beach, CA. find
  102. Zeng, Hua-Jun, Qi-Cai He, Zheng Chen, Wei-Ying Ma, and Jinwen Ma. 2004. Learning to cluster Web search results. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 210–217, Sheffield. find
  103. Zhai, ChengXiang, William W. Cohen, and John Lafferty. 2003. Beyond independent relevance: Methods and evaluation metrics for subtopic retrieval. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 10–17, Toronto. find
  104. Zhang, Benyu, Hua Li, Yi Liu, Lei Ji, Wensi Xi, Weiguo Fan, Zheng Chen, and Wei-Ying Ma. 2005. Improving Web search results using affinity graph. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005, pages 504–511, Salv… find
  105. Zhang, Xiaodan, Xiaohua Hu, and Xiaohua Zhou. 2008. A comparative evaluation of different link types on enhancing document clustering. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 555–562, Singapore. find
  106. Zhao, Ying and George Karypis. 2004. Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning, 55(3):311–331. find

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