Abstract
We propose a novel method of query expansion for Language Modeling (LM) in Information Retrieval (IR) based on the similarity of the query with sentences in the top ranked documents from an initial retrieval run. In justification of our approach, we argue that the terms in the expanded query obtained by the proposed method roughly follow a Dirichlet distribution which, being the conjugate prior of the multinomial distribution used in the LM retrieval model, helps the feedback step. IR experiments on the TREC ad-hoc retrieval test collections using the sentence based query expansion (SBQE) show a significant increase in Mean Average Precision (MAP) compared to baselines obtained using standard term-based query expansion using LM selection score and the Relevance Model (RLM). The proposed approach to query expansion for LM increases the likelihood of generation of the pseudo-relevant documents by adding sentences with maximum term overlap with the query sentences for each top ranked pseudo-relevant document thus making the query look more like these documents. A per topic analysis shows that the new method hurts less queries compared to the baseline feedback methods, and improves average precision (AP) over a broad range of queries ranging from easy to difficult in terms of the initial retrieval AP. We also show that the new method is able to add a higher number of good feedback terms (the golden standard of good terms being the set of terms added by True Relevance Feedback). Additional experiments on the challenging search topics of the TREC-2004 Robust track show that the new method is able to improve MAP by 5.7% without the use of external resources and query hardness prediction typically used for these topics.
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References
Wilkinson, R.: Effective retrieval of structured documents. In: SIGIR, pp. 311–317. Springer New York, Inc., New York (1994)
Tombros, A., Sanderson, M.: Advantages of query biased summaries in information retrieval. In: SIGIR 1998, pp. 2–10. ACM, New York (1998)
Terra, E.L., Warren, R.: Poison pills: harmful relevant documents in feedback. In: CIKM 2005, pp. 319–320. ACM, New York (2005)
Callan, J.P.: Passage-level evidence in document retrieval. In: SIGIR 1994, pp. 302–310. ACM/Springer (1994)
Allan, J.: Relevance feedback with too much data. In: SIGIR 1995, pp. 337–343. ACM Press, New York (1995)
Rocchio, J.J.: Relevance feedback in information retrieval. In: The SMART Retrieval System – Experiments in Automatic Document Processing. Prentice-Hall, Englewood Cliffs (1971)
Robertson, S.E., Walker, S., Jones, S., Hancock-Beaulieu, M.M., Gatford, M.: Okapi at TREC-3. In: Overview of the Third Text Retrieval Conference (TREC-3), pp. 109–126. NIST (1995)
Hiemstra, D.: Using Language Models for Information Retrieval. PhD thesis, Center of Telematics and Information Technology, AE Enschede (2000)
Billerbeck, B., Zobel, J.: Questioning query expansion: An examination of behaviour and parameters. In: ADC 2004, vol. 27, pp. 69–76. Australian Computer Society, Inc. (2004)
Ogilvie, P., Vorhees, E., Callan, J.: On the number of terms used in automatic query expansion. Information Retrieval 12(6), 666–679
Cao, G., Nie, J.Y., Gao, J., Robertson, S.: Selecting good expansion terms for pseudo-relevance feedback. In: SIGIR 2008, pp. 243–250. ACM, New York (2008)
Leveling, J., Jones, G.J.F.: Classifying and filtering blind feedback terms to improve information retrieval effectiveness. In: RIAO 2010, CID (2010)
Sakai, T., Manabe, T., Koyama, M.: Flexible pseudo-relevance feedback via selective sampling. ACM Transactions on Asian Language Processing 4(2), 111–135 (2005)
Robertson, S., Walker, S., Beaulieu, M., Willett, P.: Okapi at TREC-7: Automatic ad hoc, filtering, vlc and interactive track 21, 253–264 (1999)
Buckley, C., Salton, G., Allan, J., Singhal, A.: Automatic query expansion using SMART: TREC 3. In: Overview of the Third Text REtrieval Conference (TREC-3), pp. 69–80. NIST (1994)
Ponte, J.M.: A language modeling approach to information retrieval. PhD thesis, University of Massachusetts (1998)
Lavrenko, V., Croft, B.W.: Relevance based language models. In: SIGIR 2001, pp. 120–127. ACM, New York (2001)
Xu, J., Croft, W.B.: Query expansion using local and global document analysis. In: SIGIR 1996, pp. 4–11. ACM, New York (1996)
Lam-Adesina, A.M., Jones, G.J.F.: Applying summarization techniques for term selection in relevance feedback. In: SIGIR 2001, pp. 1–9. ACM, New York (2001)
Järvelin, K.: Interactive relevance feedback with graded relevance and sentence extraction: simulated user experiments. In: CIKM 2009, pp. 2053–2056. ACM, New York (2009)
Lv, Y., Zhai, C.: Positional relevance model for pseudo-relevance feedback. In: SIGIR 2010, pp. 579–586. ACM, New York (2010)
Murdock, V.: Aspects of Sentence Retrieval. PhD thesis, University of Massachusetts - Amherst (2006)
Losada, D.E.: Statistical query expansion for sentence retrieval and its effects on weak and strong queries. Inf. Retr. 13, 485–506 (2010)
Wilkinson, R., Zobel, J., Sacks-Davis, R.: Similarity measures for short queries. In: Fourth Text REtrieval Conference (TREC-4), pp. 277–285 (1995)
Blackwell, D., James, M.: Fergusson distributions via Polya urn schemes. Annals of Statistics, 353–355 (1973)
Xu, J., Croft, W.B.: Improving the effectiveness of informational retrieval with Local Context Analysis. ACM Transactions on Information Systems 18, 79–112 (2000)
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Strohman, T., Metzler, D., Turtle, H., Croft, W.B.: Indri: a language-model based search engine for complex queries. In: Online Proceedings of the International Conference on Intelligence Analysis (2005)
Mitra, M., Singhal, A., Buckley, C.: Improving automatic query expansion. In: SIGIR 1998, pp. 206–214. ACM, New York (1998)
Voorhees, E.M.: Overview of the TREC 2004 robust track. In: TREC (2004)
Harman, D., Buckley, C.: The NRRC Reliable Information Access (ria) workshop. In: SIGIR 2004, pp. 528–529. ACM, New York (2004)
Buckley, C.: Why current IR engines fail. In: SIGIR 2004, pp. 584–585. ACM, New York (2004)
Kwok, K.L., Grunfeld, L., Sun, H.L., Deng, P.: TREC 2004 robust track experiments using PIRCS. In: TREC (2004)
Amati, G., Carpineto, C., Romano, G.: Fondazione Ugo Bordoni at TREC 2004. In: TREC (2004)
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Ganguly, D., Leveling, J., Jones, G.J.F. (2011). Query Expansion for Language Modeling Using Sentence Similarities. In: Hanbury, A., Rauber, A., de Vries, A.P. (eds) Multidisciplinary Information Retrieval. IRFC 2011. Lecture Notes in Computer Science, vol 6653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21353-3_6
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