Abstract
Cluster retrieval assumes that the probability of relevance of a document should depend on the relevance of other similar documents to the same query. The goal is to find the best group of documents. Many studies have examined the effectiveness of this approach, by employing different retrieval methods or clustering algorithms, but few have investigated text representations. This paper revisits the problem of retrieving the best group of documents, from the language-modeling perspective. We analyze the advantages and disadvantages of a range of representation techniques, derive features that characterize the good document groups, and experiment with a new probabilistic representation as a first step toward incorporating these features. Empirical evaluation demonstrates that the relationship between documents can be leveraged in retrieval when a good representation technique is available, and that retrieving the best group of documents can be more effective than retrieving individual documents.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Croft, W.B.: A model of cluster searching based on classification. Information Systems 5, 189–195 (1980)
Griffiths, A., Luckhurst, H.C., Willett, P.: Using interdocument similarity information in document retrieval systems. Journal of the American Society for Information Science 37, 3–11 (1986)
Hearst, M.A., Pedersen, J.O.: Re-examining the cluster hypothesis: Scatter/Gather on retrieval results. In: SIGIR 1996, pp. 76–84 (1996)
Jardine, N., van Rijsbergen, C.J.: The use of hierarchical clustering in information retrieval. Information Storage and Retrieval 7, 217–240 (1971)
Krovetz, R.: Viewing Morphology as an Inference Process. In: SIGIR 1993, pp. 191–203 (1993)
Kurland, O., Lee, L.: Corpus structure, language models, and ad hoc information retrieval. In: Proceedings of SIGIR 2004 conference, pp. 194–201 (2004)
Leuski, A.: Evaluating Document Clustering for Interactive Information Retrieval. In: Proceedings of CIKM 2001 conference, pp. 33–40 (2001)
Liu, X., Croft, W.B.: Cluster-based retrieval using language models. In: Proceedings of SIGIR 2004 conference, pp. 186–193 (2004)
Liu, X.: Cluster-based retrieval from a language-modeling perspective. In: The Doctoral Consortium of SIGIR 2006 conference, pp. 737–738 (2006), Abstract in SIGIR 2006 Proceedings
Liu, X., Croft, W.B.: Representing clusters for retrieval. In: Proceedings of SIGIR 2006 conference, pp. 671–672 (2006)
Miller, D., Leek, T., Schwartz, R.: A hidden Markov model information retrieval system. In: SIGIR 1999, pp. 214–221 (1999)
Ponte, J., Croft, W.B.: A language modeling approach to information retrieval. In: SIGIR 1998, pp. 275–281 (1998)
Robertson, S.E.: The probability ranking principle in IR. Journal of Documentation 33, 294–304 (1977)
Tao, T., Wang, X., Mei, Q., Zhai, C.: Language model information retrieval with document expansion. In: Proceedings of HLT/NAACL 2006 (2006)
Tombros, A., Villa, R., Van Rijsbergen, C.J.: The effectiveness of query-specific hierarchic clustering in information retrieval. Information Processing and Management 38, 559–582 (2002)
van Rijsbergen, C.J., Croft, W.B.: Document clustering: An evaluation of some experiments with the Cranfield 1400 collection. Information Processing & Management 11, 171–182 (1975)
van Rijsbergen, C.J., Sparck Jones, K.: A test for the separation of relevant and non-relevant documents in experimental retrieval collections. Journal of Documentation 29, 251–257 (1973)
Voorhees, E.M.: The cluster hypothesis revisited. In: SIGIR 1985, pp. 188–196 (1985)
Voorhees, E.M.: The TREC robust retrieval track. SIGIR Forum 39(1) (2005)
Willet, P.: Query specific automatic document classification. International Forum on Information and Documentation 10(2), 28–32 (1985)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, X., Croft, W.B. (2008). Evaluating Text Representations for Retrieval of the Best Group of Documents. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_43
Download citation
DOI: https://doi.org/10.1007/978-3-540-78646-7_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78645-0
Online ISBN: 978-3-540-78646-7
eBook Packages: Computer ScienceComputer Science (R0)