Abstract
Information about top-ranked documents plays a key role to improve retrieval performance. One of the most common strategies which exploits this kind of information is relevance feedback. Few works have investigated the role of negative feedback on retrieval performance. This is probably due to the difficulty of dealing with the concept of non-relevant document. This paper proposes a novel approach to document re-ranking, which relies on the concept of negative feedback represented by non-relevant documents. In our model the concept of non-relevance is defined as a quantum operator in both the classical Vector Space Model and a Semantic Document Space. The latter is induced from the original document space using a distributional approach based on Random Indexing. The evaluation carried out on a standard document collection shows the effectiveness of the proposed approach and opens new perspectives to address the problem of quantifying the concept of non-relevance.
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References
Agirre, E., Di Nunzio, G.M., Mandl, T., Otegi, A.: CLEF 2009 Ad Hoc Track Overview: Robust-WSD Task. In: Peters, C., Di Nunzio, G.M., Kurimo, M., Mostefa, D., Penas, A., Roda, G. (eds.) CLEF 2009. LNCS, vol. 6241, pp. 36–49. Springer, Heidelberg (2010)
Birkhoff, G., von Neumann, J.: The logic of quantum mechanics. Annals of Mathematics 37(4), 823–843 (1936)
Caputo, A., Basile, P., Semeraro, G.: From fusion to re-ranking: a semantic approach. In: Crestani, F., Marchand-Maillet, S., Chen, H.H., Efthimiadis, E.N., Savoy, J. (eds.) SIGIR, pp. 815–816. ACM, New York (2010)
Danilowicz, C., Balinski, J.: Document ranking based upon Markov chains. Information Processing & Management 37(4), 623–637 (2001)
Dasgupta, S., Gupta, A.: An elementary proof of a theorem of Johnson and Lindenstrauss. Random Structures & Algorithms 22(1), 60–65 (2003)
Diaz, F.: Regularizing ad hoc retrieval scores. In: CIKM 2005: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 672–679. ACM, New York (2005)
Harris, Z.: Mathematical Structures of Language. Interscience, New York (1968)
Kanerva, P.: Sparse Distributed Memory. MIT Press, Cambridge (1988)
Kozorovitzky, A., Kurland, O.: From ”identical” to ”similar”: Fusing retrieved lists based on inter-document similarities. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 212–223. Springer, Heidelberg (2009)
Kurland, O.: Re-ranking search results using language models of query-specific clusters. Information Retrieval 12(4), 437–460 (2009)
Kurland, O., Lee, L.: Corpus structure, language models, and ad hoc information retrieval. In: SIGIR 2004: Proceedings of the 27th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 194–201. ACM, New York (2004)
Landauer, T.K., Dumais, S.T.: A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. Psychological Review 104(2), 211–240 (1997)
Liu, X., Croft, W.B.: Cluster-based retrieval using language models. In: SIGIR 2004: Proceedings of the 27th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 186–193. ACM, New York (2004)
van Rijsbergen, C.J.: Information Retrieval. Butterworth, London (1979)
Robertson, S., Zaragoza, H., Taylor, M.: Simple BM25 extension to multiple weighted fields. In: CIKM 2004: Proceedings of the Thirteenth ACM Int. Conf. on Information and Knowledge Management, pp. 42–49. ACM, New York (2004)
Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. Knowledge Engineering Review 18(2), 95–145 (2003)
Sahlgren, M.: The Word-Space Model: Using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces. Ph.D. thesis, Stockholm University, Department of Linguistics (2006)
Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science 41(4), 288–297 (1990)
Singhal, A., Mitra, M., Buckley, C.: Learning routing queries in a query zone. In: SIGIR 1997: Proceedings of the 20th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 25–32. ACM, New York (1997)
Tseng, Y., Tsai, C., Chuang, Z.: On the robustness of document re-ranking techniques: a comparison of label propagation, knn, and relevance feedback. In: Proceedings of NTCIR-6 Workshop (2007)
Wang, X., Fang, H., Zhai, C.: A study of methods for negative relevance feedback. In: SIGIR 2008: Proceedings of the 31st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 219–226. ACM, New York (2008)
Widdows, D., Peters, S.: Word vectors and quantum logic: Experiments with negation and disjunction. Mathematics of language (8), 141–154 (2003)
Widdows, D.: Orthogonal negation in vector spaces for modelling word-meanings and document retrieval. In: ACL 2003: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, pp. 136–143. Association for Computational Linguistics, Morristown (2003)
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Basile, P., Caputo, A., Semeraro, G. (2011). Negation for Document Re-ranking in Ad-hoc Retrieval. In: Amati, G., Crestani, F. (eds) Advances in Information Retrieval Theory. ICTIR 2011. Lecture Notes in Computer Science, vol 6931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23318-0_26
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DOI: https://doi.org/10.1007/978-3-642-23318-0_26
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