Academia.eduAcademia.edu

Outline

Title
Abstract
Probabilistic Relaxation Labeling Methods
Spreading Activation Methods
Special Cases
Experiment Design Data Set and Baseline Methods
Result Analysis
Comparison of Estimation Methods
Traditional Smoothing Methods
Term Propagation Smoothing Method
Data Sets and Baseline Method
One-Step Versus Multiple-Step Smoothing Results
Related Work
Introduction
Previous Work
Basic Query Translation Method
Propagation Method
Data Set and Queries
Query Translation Using Propagation Method
Sensitivity Analysis
Summary
Future Directions
Study the Characteristics of the Framework
References
All Topics
Computer Science
Data Science

Probabilistic score propagation in information retrieval

Profile image of Azadeh ShakeryAzadeh Shakery
visibility

…

description

127 pages

link

1 file

Abstract

Information retrieval techniques deal with different units of information such as terms, topics or documents. There usually exist explicit or implicit link structures between different items of each unit or between items across different units. For example hyperlinks between pages in a hypertext collection are explicit structures, while the links between terms in a co-occurrence network are implicit structures. Many of the traditional information retrieval methods only use the content information of the items for retrieval purposes and overlook the link structures. Those that use the link structures also do not fully exploit the discrimination power of contents as well as all useful link information. In this thesis, we propose a general probabilistic score propagation framework for combining content and link information, which can fully take advantage of content information and the link

Related papers

Probabilistic Models in Information Retrieval
Norbert Fuhr

The Computer Journal, 1992

In this paper, an introduction and survey over probabilistic information retrieval (IR) is given. First, the basic concepts of this approach are described: the probability-ranking principle shows that optimum retrieval quality can be achieved under certain assumptions; a conceptual model for IR along with the corresponding event space clarify the interpretation of the probabilistic parameters involved. For the estimation of these parameters, three different learning strategies are distinguished, namely query-related, document-related and description-related learning. As a representative for each of these strategies, a specific model is described. A new approach regards IR as uncertain inference; here, imaging is used as a new technique for estimating the probabilistic parameters, and probabilistic inference networks support more complex forms of inference. Finally, the more general problems of parameter estimations, query expansion and the development of models for advanced document representations are discussed.

View PDFchevron_right
Xhits: Learning to Rank in a Hyperlinked Structure
Francisco Benjamim

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, 2011

The explosive growth and the widespread accessibility of the Web has led to a surge of research activity in the area of information retrieval on the WWW. This is a huge and rich environment where the web pages can be viewed as a large community of elements that are connected through links due to several issues. The HITS approach introduces two basic concepts, hubs and authorities, which reveal some hidden semantic information from the links. In this paper, we review the XHITS, a generalization of HITS, which expands the model from two to several concepts and present a new Machine Learning algorithm to calibrate an XHITS model. The new learning algorithm uses latent feature concepts. Furthermore, we provide some illustrative examples and empirical tests. Our findings indicate that the new learning approach provides a more accurate XHITS model.

View PDFchevron_right
Probabilistic models of text and link structure for hypertext classification
Lise Getoor

2001

Most text classification methods treat each document as an independent instance. However, in many text domains, documents are linked and the topics of linked documents are correlated. For example, web pages of related topics are often connected by hyperlinks and scientific papers from related fields are commonly linked by citations.

View PDFchevron_right
Re-ranking method based on inter-document distances
Jarosław Baliński

Information Processing and Management, 2005

Lately there has been intensive research into the possibilities of using additional information about documents (such as hyperlinks) to improve retrieval effectiveness. It is called data fusion, based on the intuitive principle that different document and query representations or different methods lead to a better estimation of the documents' relevance scores.

View PDFchevron_right
A study of relevance propagation for web search
Tao Qin

2005

Different from traditional information retrieval, both content and structure are critical to the success of Web information retrieval. In recent years, many relevance propagation techniques have been proposed to propagate content information between web pages through web structure to improve the performance of web search. In this paper, we first propose a generic relevance propagation framework, and then provide a comparison study on the effectiveness and efficiency of various representative propagation models that can be derived from this generic framework. We come to many conclusions that are useful for selecting a propagation model in real-world search applications, including 1) sitemapbased propagation models outperform hyperlink-based models in sense of both effectiveness and efficiency, and 2) sitemap-based term propagation is easier to be integrated into real-world search engines because of its parallel offline implementation and acceptable complexity. Some other more detailed study results are also reported in the paper.

View PDFchevron_right
A Score based Web Page Ranking Algorithm
Shaily Kabir

International Journal of Computer Applications, 2015

With the explosive growth of information in the Web, users face difficulties while finding their desired information. Search engine helps the user by retrieving useful information from this huge collection based on his/her search query and presents a list of relevant web pages as a search result. However, without proper ranking of pages in the result through the relevancy of pages to the search query, the user may need to explore the whole list for discovering the appropriate page(s), thereby involving huge search time. Although a number of ranking algorithms such as HITS, PageRank, Weighted PageRank and etc., are developed to assist the search engine, but none of them provides page ranking with high accuracy. In this paper, we propose a scorebased web page ranking algorithm involving web content mining and usage information of the pages. Our algorithm considers both syntactical and semantic matches of the search query to the pages. For a web page, syntactical score is calculated based on the total number of exact matches of the search words in the page. Besides, semantic score is measured using synonym matches of the search words. Moreover, we incorporate the usage information of the pages as page popularity in order to comprise the user interest in the ranking order. The total relevant score of each page is calculated using the summation of the syntactical and semantic scores of the page and its page popularity. Finally, the pages are ranked according to their total relevant score. Based on several performance evaluation measures, experimental results show considerable improvement in the page ranking using our algorithm as compared to other known approaches.

View PDFchevron_right
Regularizing query-based retrieval scores
Fernando Díaz

2007

We adapt the cluster hypothesis for score-based information retrieval by claiming that closely related documents should have similar scores. Given a retrieval from an arbitrary system, we describe an algorithm which directly optimizes this objective by adjusting retrieval scores so that topically related documents receive similar scores. We refer to this process as score regularization. Because score regularization operates on retrieval scores, regardless of their origin, we can apply the technique to arbitrary initial retrieval rankings. Document rankings derived from regularized scores, when compared to rankings derived from un-regularized scores, consistently and significantly result in improved performance given a variety of baseline retrieval algorithms. We also present several proofs demonstrating that regularization generalizes methods such as pseudo-relevance feedback, document expansion, and cluster-based retrieval. Because of these strong empirical and theoretical results, we argue for the adoption of score regularization as general design principle or post-processing step for information retrieval systems.

View PDFchevron_right
Pertinent Information retrieval based on Possibilistic Bayesian network : origin and possibilistic perspective
B. Ayeb, Mohamed Nazih OMRI

In this paper we present a synthesis of work performed on tow information retrieval models:

View PDFchevron_right
Saripod: A Possibilistic System for Web Information Retrieval
Fabrice Evrard

2007

This paper presents a web information retrieval system based on Hierarchical Small-Worlds (HSW) and Possibilistic Networks (PN). The first HSW consists in structuring the "Google" search results in dense zones of web pages which strongly depend on each other. We thus reveal dense clouds of pages which "speak" more or less about the same subject and which all strongly answer the user’s query. The goal of the second HSW consists in considering the query as multiple in the sense that we don’t seek only the keyword in the web pages but also its synonyms. The PN generates the mixing of these two HSW in order to organize the searched documents according to user’s preferences. Indeed, SARIPOD is a new approach for Information Retrieval Model based on possibility and necessity measures. This model encodes relationship dependencies existing between query terms and web documents through naïve possibilistic networks and quantifies these relationships by two measures: possib...

View PDFchevron_right
Modeling the Score Distributions of Relevant and Non-Relevant Documents
Evangelos Kanoulas
View PDFchevron_right

References (105)

  1. Review of "trec -experiment and evaluation in information retrieval" by ellen m. voorhees, donna k. harman (eds.), the mit press, cambridge, ma, 2005. Inf. Process. Manage., 43(1):285-287, 2007. Reviewer-Gheorghe Muresan.
  2. V.N. Anh and A. Moffat. Melbourne university 2004: Terabyte and web tracks. In Proceed- ings of the TREC Conference, 2004.
  3. Krisztian Balog, Leif Azzopardi, and Maarten de Rijke. Formal models for expert finding in enterprise corpora. In SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 43-50, New York, NY, USA, 2006. ACM.
  4. Krishna Bharat and Monika R. Henzinger. Improved algorithms for topic distillation in a hyperlinked environment. In SIGIR '98: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 104-111, New York, NY, USA, 1998. ACM Press.
  5. Krishna Bharat and George A. Mihaila. When experts agree: using non-affiliated experts to rank popular topics. ACM Trans. Inf. Syst., 20(1):47-58, 2002.
  6. Allan Borodin, Gareth O. Roberts, Jeffrey S. Rosenthal, and Panayiotis Tsaparas. Link analysis ranking: algorithms, theory, and experiments. ACM Trans. Inter. Tech., 5(1):231- 297, 2005.
  7. S. Chakrabarti, B. Dom, D. Gibson, J. Kleinberg, P. Raghavan, and S. Rajagopalan. Au- tomatic resource list compilation by analyzing hyperlink structure and associated text. In Proceedings of the 7th International World Wide Web Conference, 1998.
  8. S. Chakrabarti, B. Dom, D. Gibson, S.R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Spectral filtering for resource discovery. In ACM SIGIR workshop on Hy- pertext Information Retrieval on the Web, 1998.
  9. Soumen Chakrabarti, Byron Dom, and Piotr Indyk. Enhanced hypertext categorization using hyperlinks. In SIGMOD '98: Proceedings of the 1998 ACM SIGMOD international conference on Management of data, volume 27, pages 307-318. ACM Press, 1998.
  10. Paul R. Cohen and Rick Kjeldsen. Information retrieval by constrained spreading activation in semantic networks. Inf. Process. Manage., 23(4):255-268, 1987.
  11. N. Craswell and D. Hawking. Overview of the trec-2002 web track. In Proceedings of the TREC Conference, 2002.
  12. N. Craswell and D. Hawking. Overview of the trec-2003 web track. In Proceedings of the TREC Conference, 2003.
  13. N. Craswell and D. Hawking. Overview of the trec-2004 web track. In Proceedings of the TREC Conference, 2004.
  14. Nick Craswell and Martin Szummer. Random walks on the click graph. In SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and de- velopment in information retrieval, pages 239-246, New York, NY, USA, 2007. ACM.
  15. Fabio Crestani and Puay Leng Lee. Searching the web by constrained spreading activation. Inf. Process. Manage., 36(4):585-605, 2000.
  16. W. B. Croft, T. J. Lucia, and P. R. Cohen. Retrieving documents by plausible inference: a priliminary study. In SIGIR '88: Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval, pages 481-494, New York, NY, USA, 1988. ACM Press.
  17. W. B. Croft, T. J. Lucia, J. Cringean, and P. Willett. Retrieving documents by plausible inference: an experimental study. Inf. Process. Manage., 25(6):599-614, 1989.
  18. Brian D. Davison. Toward a unification of text and link analysis. In SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 367-368, New York, NY, USA, 2003. ACM Press.
  19. Hui Fang and ChengXiang Zhai. Probabilistic models for expert finding. In ECIR, pages 418-430, 2007.
  20. Martin Franz and J. Scott McCarley. Arabic information retrieval at ibm. In TREC, 2002.
  21. Martin Franz, J. Scott McCarley, and Salim Roukos. Ad hoc and multilingual information retrieval at IBM. In Text REtrieval Conference, pages 104-115, 1998.
  22. H. P. Frei and D. Stieger. The use of semantic links in hypertext information retrieval. Inf. Process. Manage., 31(1):1-13, 1995.
  23. Mark Edwin Frisse. Searching for information in a hypertext medical handbook. In HY- PERTEXT '87: Proceedings of the ACM conference on Hypertext, pages 57-66, New York, NY, USA, 1987. ACM.
  24. Norbert Fuhr. Probabilistic models in information retrieval. The Computer Journal, 35(3):243-255, 1992.
  25. Pascale Fung and Lo Yuen Yee. An ir approach for translating new words from nonparallel, comparable texts. In Proceedings of the 17th international conference on Computational linguistics, pages 414-420, Morristown, NJ, USA, 1998. Association for Computational Linguistics.
  26. Richard Furuta, Catherine Plaisant, and Ben Shneiderman. A spectrum of automatic hyper- text constructions. Hypermedia, 1(2):179-195, 1989.
  27. E. Garfield. Citation indexes for science. Science, 129, 1955.
  28. Lise Getoor and Christopher P. Diehl. Link mining: a survey. SIGKDD Explor. Newsl., 7(2):3-12, 2005.
  29. Zoubin Ghahramani. Learning dynamic Bayesian networks. Lecture Notes in Computer Science, 1387:168-197, 1998.
  30. G. Grimmett and D. Stirzaker. Probability and random processes. In Oxford University Press, 1989.
  31. C. O. Hartman. Virtual Muse: Experiments in Computer Poetry (Wesleyan Poetry). Wes- leyan University Press, 1996.
  32. T. H. Haveliwala. Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Transactions on Knowledge and Data Engineering, 15(4):784-796, 2003.
  33. Djoerd Hiemstra and Wessel Kraaij. Twenty-one at trec7: Ad-hoc and cross-language track. In Text REtrieval Conference, pages 174-185, 1998.
  34. R. A. Hummel and S. W. Zucker. On the foundation of relaxation labeling processes. IEEE Trans. on Pattern Analysis and Machine Intelligence, 5:267-287, 1983.
  35. J. Kamps, G. Mishne, and M. de Rijke. Language models for searching in web corpora. In Proceedings of the TREC Conference, 2004.
  36. Jon M. Kleinberg. Authoritative sources in a hyperlinked environment. J. ACM, 46(5):604- 632, 1999.
  37. Oren Kurland and Lillian Lee. Corpus structure, language models, and ad hoc information retrieval. In SIGIR '04: Proceedings of the 27th annual international ACM SIGIR confer- ence on Research and development in information retrieval, pages 194-201, New York, NY, USA, 2004. ACM Press.
  38. Oren Kurland and Lillian Lee. Pagerank without hyperlinks: structural re-ranking using links induced by language models. In SIGIR '05: Proceedings of the 28th annual interna- tional ACM SIGIR conference on Research and development in information retrieval, pages 306-313, New York, NY, USA, 2005. ACM Press.
  39. John Lafferty and Chengxiang Zhai. Document language models, query models, and risk minimization for information retrieval. In SIGIR '01: Proceedings of the 24th annual in- ternational ACM SIGIR conference on Research and development in information retrieval, pages 111-119, New York, NY, USA, 2001. ACM Press.
  40. R. Larson. Bibliometrics of the world wide web: An exploratory analysis of the intellectual structure of cyberspace. In Annual Meeting of the American Society of Information Science, 1996.
  41. Victor Lavrenko and W. Bruce Croft. Relevance based language models. In SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and de- velopment in information retrieval, pages 120-127, New York, NY, USA, 2001. ACM.
  42. R. Lempel and S. Moran. The stochastic approach for link-structure analysis (salsa) and the tkc effect. Comput. Netw., 33(1-6):387-401, 2000.
  43. Xiaoyong Liu and W. Bruce Croft. Cluster-based retrieval using language models. In SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 186-193, New York, NY, USA, 2004. ACM Press.
  44. Massimo Marchiori. The quest for correct information on the Web: Hyper search engines. Computer Networks and ISDN Systems, 29(8-13):1225-1236, 1997.
  45. Hiroshi Masuichi, Raymond Flournoy, Stefan Kaufmann, and Stanley Peters. A bootstrap- ping method for extracting bilingual text pairs. In Proceedings of the 18th conference on Computational linguistics, pages 1066-1070, Morristown, NJ, USA, 2000. Association for Computational Linguistics.
  46. Fabien Mathieu and Mohamed Bouklit. The effect of the back button is a random walk: Application for pagerank. In Proceedings of the 13th International World Wide Web Con- ference, 2004.
  47. S. Meyn. Control Techniques for Complex Networks. Cambridge University Press, 2007.
  48. S. Meyn and R. Tweedie. Markov Chains and Stochastic Stability. Springer-Verlag, 1993.
  49. David R. H. Miller, Tim Leek, and Richard M. Schwartz. A hidden markov model infor- mation retrieval system. In SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 214-221, New York, NY, USA, 1999. ACM Press.
  50. Dharmendra S. Modha and W. Scott Spangler. Clustering hypertext with applications to web searching. In HYPERTEXT '00: Proceedings of the eleventh ACM on Hypertext and hypermedia, pages 143-152, New York, NY, USA, 2000. ACM Press.
  51. Dragos Stefan Munteanu and Daniel Marcu. Improving machine translation performance by exploiting non-parallel corpora. Comput. Linguist., 31(4):477-504, 2005.
  52. Andrew Y. Ng, Alice X. Zheng, and Michael I. Jordan. Stable algorithms for link analysis. In SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 258-266, New York, NY, USA, 2001. ACM Press.
  53. Douglas W. Oard and Anne R. Diekema. Cross-language information retrieval. In Annual Review of Information Science and Technology, volume 33, pages 223 -256, 1998.
  54. Douglas W. Oard and Fredric C. Gey. The trec 2002 arabic/english clir track. In TREC, 2002.
  55. Paul Ogilvie and Jamie Callan. Combining document representations for known-item search. In SIGIR '03: Proceedings of the 26th annual international ACM SIGIR confer- ence on Research and development in informaion retrieval, pages 143-150, New York, NY, USA, 2003. ACM.
  56. Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library, 1998.
  57. Judea Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1988.
  58. E. Picchi and C. Peters. Cross language information retrieval: A system for comparable corpus querying. In Workshop on Cross-Linguistic Information Retrieval, SIGIR'96, pages 24 -33, 1996.
  59. Peter Pirolli, James Pitkow, and Ramana Rao. Silk from a sow's ear: Extracting usable structures from the web. In Proc. ACM Conf. Human Factors in Computing Systems, CHI. ACM Press, 1996.
  60. Jay M. Ponte and W. Bruce Croft. A language modeling approach to information retrieval. In SIGIR '98: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 275-281, New York, NY, USA, 1998. ACM.
  61. Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Zheng Chen, and Wei-Ying Ma. A study of rele- vance propagation for web search. In SIGIR '05: Proceedings of the 28th annual interna- tional ACM SIGIR conference on Research and development in information retrieval, pages 408-415, New York, NY, USA, 2005. ACM Press.
  62. Prabhakar Raghavan Rajeev Motwani. Randomized Algorithms. Cambridge University Press, 1995.
  63. Reinhard Rapp. Identifying word translations in non-parallel texts. In Proceedings of the 33rd annual meeting on Association for Computational Linguistics, pages 320-322, Mor- ristown, NJ, USA, 1995. Association for Computational Linguistics.
  64. Mathew Richardson and Pedro Domingos. The intelligent surfer: Probabilistic combination of link and content information in pagerank. In Advances in Neural Information Processing Systems, 2002.
  65. Stephen Robertson. Threshold setting and performance optimization in adaptive filtering. Information Retrieval, 5(2-3):239-256, 2002.
  66. Stephen E. Robertson and Karen Sparck Jones. Relevance weighting of search terms. Jour- nal of the American Society for Information Science, 27(3):129-146, 1976.
  67. J ROCCHIO. Relevance feedback in information retrieval. In In The SMART Retrieval System: Experiments in Automatic Document Processing, pages 313-323. PrenticeHall, 1971.
  68. Fatiha Sadat, Masatoshi Yoshikawa, and Shunsuke Uemura. Bilingual terminology acqui- sition from comparable corpora and phrasal translation to cross-language information re- trieval. In ACL '03: Proceedings of the 41st Annual Meeting on Association for Computa- tional Linguistics, pages 141-144, Morristown, NJ, USA, 2003. Association for Computa- tional Linguistics.
  69. Gerard Salton. Associative document retrieval techniques using bibliographic information. J. ACM, 10(4):440-457, 1963.
  70. Gerard Salton. Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1989.
  71. Gerard Salton and Chris Buckley. On the use of spreading activation methods in automatic information retrieval. Technical report, Ithaca, NY, USA, 1988.
  72. Gerard Salton and Chris Buckley. Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41(4):288-297, 1990.
  73. Gerard Salton and Michael J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986.
  74. Gerard Salton, C. S. Yang, and C. T. Yu. A theory of term importance in automatic text analysis. Journal of the American Society for Information Science, 26(1):33-44, 1975.
  75. Jacques Savoy. Bayesian inference networks and spreading activation in hypertext systems. Inf. Process. Manage., 28(3):389-406, 1992.
  76. Jacques Savoy. Ranking schemes in hybrid boolean systems: a new approach. J. Am. Soc. Inf. Sci., 48(3):235-253, 1997.
  77. Jacques Savoy and Yves Rasolofo. Report on the trec 11 experiment: Arabic, named page and topic distillation searches. In TREC, 2002.
  78. Azadeh Shakery and Chengxiang Zhai. Relevance propagation for topic distillation uiuc trec 2003 web track experiments. In Proceedings of the TREC Conference, 2003.
  79. P. Sheridan, J. Ballerini, and P. Schauble. Building a large multilingual test collection from comparable news documents. In G. Grefenstette, editor, Cross-Language Information Re- trieval, Boston, Massachusetts, 1998. Kluwer Academic Publishers.
  80. Luo Si and Jamie Callan. Modeling search engine effectiveness for federated search. In SI- GIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 83-90, New York, NY, USA, 2005. ACM.
  81. Amit Singhal, Chris Buckley, and Mandar Mitra. Pivoted document length normalization. In SIGIR '96: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, pages 21-29, New York, NY, USA, 1996. ACM.
  82. H. Small. Co-citation in the scientific literature: a new measure of the relationship between two documents. The American Society of Information Science, 24, 1973.
  83. R. Song, J. R. Wen, S. M. Shi, T. Y. Xin, G. M. abd Liu, T. Qin, X. Zheng, J. Y. Zhang, G. R. Xue, and W. Y. Ma. Microsoft research asia at web track and terabyte track of trec 2004. In Proceedings of the TREC Conference, 2004.
  84. Marcin Sydow. Random surfer with back step. In Proceedings of the 13th International World Wide Web Conference, 2004.
  85. Tao Tao, Xuanhui Wang, Qiaozhu Mei, and ChengXiang Zhai. Language model information retrieval with document expansion. In HLT-NAACL, 2006.
  86. Tao Tao and ChengXiang Zhai. Mining comparable bilingual text corpora for cross- language information integration. In KDD '05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pages 691-696, New York, NY, USA, 2005. ACM Press.
  87. Anastasios Tombros. The effectiveness of query-based hierarchic clustering of documents for information retrieval. Technical report, Glasgow : University of Glasgow, 2002.
  88. T. Tomiyama, K. Karoji, T. Kondo, Y. Kakuta, and T. Takagi. Meiji university web, novelty and genomics track experiments. In Proceedings of the TREC Conference, 2004.
  89. Stephen Tomlinson. Experiments in named page finding and arabic retrieval with humming- bird searchservertm at trec 2002. In TREC, 2002.
  90. Howard Turtle and W. Bruce Croft. Evaluation of an inference network-based retrieval model. ACM Trans. Inf. Syst., 9(3):187-222, 1991.
  91. C. J. van Rijsbergen. A theoretical basis for the use of co-occurrence data in information retrieval. Journal of Documentation, 33:106-119, June 1977.
  92. C. J. Van Rijsbergen. Information Retrieval, 2nd edition. Dept. of Computer Science, University of Glasgow, 1979.
  93. C. J. van Rijsbergen. A non-classical logic for information retrieval. The Computer Journal, 29(6):481-485, 1986.
  94. Ellen M. Voorhees. The cluster hypothesis revisited. In SIGIR '85: Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval, pages 188-196, New York, NY, USA, 1985. ACM.
  95. Ross Wilkinson and Alan F. Smeaton. Automatic link generation. ACM Comput. Surv., page 27, 1999.
  96. Peter Willett. Recent trends in hierarchic document clustering: a critical review. Inf. Pro- cess. Manage., 24(5):577-597, 1988.
  97. S. K. M. Wong and Y. Y. Yao. On modeling information retrieval with probabilistic infer- ence. ACM Trans. Inf. Syst., 13(1):38-68, 1995.
  98. Jinxi Xu and W. Bruce Croft. Query expansion using local and global document analysis. In SIGIR '96: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, pages 4-11, New York, NY, USA, 1996. ACM.
  99. F. Zanettin. Bilingual comparable corpora and the training of translators. In Laviosa, Sara. (ed.) META, 43:4, Special Issue. The corpus-based approach: a new paradigm in transla- tion studies, pages 616-630, 1998.
  100. H. Zaragoza, N. Craswell, M. Taylor, S. Saria, and S. Robertson. Microsoft cambridge at trec 13: Web and hard tracks. In Proceedings of the TREC Conference, 2004.
  101. Chengxiang Zhai and John Lafferty. Model-based feedback in the language modeling ap- proach to information retrieval. In CIKM '01: Proceedings of the tenth international con- ference on Information and knowledge management, pages 403-410, New York, NY, USA, 2001. ACM Press.
  102. Chengxiang Zhai and John Lafferty. Model-based feedback in the language modeling ap- proach to information retrieval. In CIKM '01: Proceedings of the tenth international con- ference on Information and knowledge management, pages 403-410, New York, NY, USA, 2001. ACM Press.
  103. Chengxiang Zhai and John Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 334-342, New York, NY, USA, 2001. ACM Press.
  104. ChengXiang Zhai and John Lafferty. Two-stage language models for information retrieval. In SIGIR '02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 49-56, New York, NY, USA, 2002. ACM.
  105. Z. Zhou, Y. Guo, B. Wang, X. Cheng, H. Xu, and G. Zhang. Trec 2004 web track experi- ments at cas-ict. In Proceedings of the TREC Conference, 2004.

Related papers

A probabilistic relevance propagation model for hypertext retrieval
Azadeh Shakery

Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06, 2006

A major challenge in developing models for hypertext retrieval is to effectively combine content information with the link structure available in hypertext collections. Although several link-based ranking methods have been developed to improve retrieval results, none of them can fully exploit the discrimination power of contents as well as fully exploit all useful link structures. In this paper, we propose a general relevance propagation framework for combining content and link information. The framework gives a probabilistic score to each document defined based on a probabilistic surfing model. Two main characteristics of our framework are our probabilistic view on the relevance propagation model and propagation through multiple sets of neighbors. We compare eight different models derived from the probabilistic relevance propagation framework on two standard TREC Web test collections. Our results show that all the eight relevance propagation models can outperform the baseline content only ranking method for a wide range of parameter values, indicating that the relevance propagation framework provides a general, effective and robust way of exploiting link information. Our experiments also show that using multiple neighbor sets outperforms using just one type of neighbors significantly and taking a probabilistic view of propagation provides guidance on setting propagation parameters.

View PDFchevron_right
Relevance Propagation Model for Large Hypertext Documents Collections
Bich-Liên DOAN

2007

Web search engines have become indispensable in our daily life to help us finding the information we need. Several search tools, for instance Google, use links to select the matching documents against a query. In this paper, we propose a new ranking function that combines content and link rank based on propagation of scores over links. This function propagates scores from source pages to destination pages in relation with query terms. We assessed our ranking function with experiments over two test collections WT10g and GOV. We conclude that propagating link scores according to query terms provides significant improvement for information retrieval.

View PDFchevron_right
Modeling score distributions in information retrieval
Stephen Robertson, Jaap Kamps, Avi Arampatzis

Information Retrieval, 2011

We review the history of modeling score distributions, focusing on the mixture of normal-exponential by investigating the theoretical as well as the empirical evidence supporting its use. We discuss previously suggested conditions which valid binary mixture models should satisfy, such as the Recall-Fallout Convexity Hypothesis, and formulate two new hypotheses considering the component distributions under some limiting conditions of parameter values. From all the mixtures suggested in the past, the current theoretical argument points to the two gamma as the most-likely universal model, with the normal-exponential being a usable approximation. Beyond the theoretical contribution, we provide new experimental evidence showing vector space or geometric models, and BM25, as being "friendly" to the normal-exponential, and that the non-convexity problem that the mixture possesses is practically not severe.

View PDFchevron_right
Score Distributions in Information Retrieval
Stephen Robertson

Lecture Notes in Computer Science, 2009

We review the history of modeling score distributions, focusing on the mixture of normal-exponential by investigating the theoretical as well as the empirical evidence supporting its use. We discuss previously suggested conditions which valid binary mixture models should satisfy, such as the Recall-Fallout Convexity Hypothesis, and formulate two new hypotheses considering the component distributions under some limiting conditions of parameter values. From all the mixtures suggested in the past, the current theoretical argument points to the two gamma as the most-likely universal model, with the normal-exponential being a usable approximation. Beyond the theoretical contribution, we provide new experimental evidence showing vector space or geometric models, and BM25, as being "friendly" to the normal-exponential, and that the non-convexity problem that the mixture possesses is practically not severe.

View PDFchevron_right
A Unified Probabilistic Framework for Web Page Scoring Systems
Marco Gori

IEEE Transactions on Knowledge and Data Engineering, 2004

The definition of efficient page ranking algorithms is becoming an important issue in the design of the query interface of Web search engines. Information flooding is a common experience especially when broad topic queries are issued. Queries containing only one or two keywords usually match a huge number of documents, while users can only afford to visit the first positions of the returned list, which do not necessarily refer to the most appropriate answers.

View PDFchevron_right

Related topics

  • Computer Science
  • Information Retrieval
  • Link analysis
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved