Julio Gonzalo
Jun Xu
ABSTRACT
Recently, significant progress has been made in research on what we call semantic matching (SM), in web search, question answering, online advertisement, cross-language information retrieval, and other tasks. Advanced technologies based on machine learning have been developed. Let us take Web search as example of the problem that also pervades the other tasks. When comparing the textual content of query and documents, Web search still heavily relies on the term-based approach, where the relevance scores between queries and documents are calculated on the basis of the degree of matching between query terms and document terms. This simple approach works rather well in practice, partly because there are many other signals in web search (hypertext, user logs, etc.) that complement it. However, when considering the long tail of web searches, it can suffer from data sparseness, e.g., Trenton does not match New Jersey Capital. Query document mismatches occur when searcher and author use different terms (representations), and this phenomenon is prevalent due to the nature of human language.
- E. Amigó, J. C. de Albornoz, I. Chugur, A. Corujo, J. Gonzalo, T. Martín, E. Meij, M. de Rijke, and D. Spina. Overview of replab 2013: Evaluating online reputation monitoring systems. In Information Access Evaluation. Multilinguality, Multimodality, and Visualization, pages 333--352. Springer, 2013.Google Scholar
- M. Diab, T. Baldwin, and M. Baroni, editors. Second Joint Conference on Lexical and Computational Semantics. Association for Computational Linguistics, Atlanta, Georgia, USA, June 2013.Google Scholar
- A. Moschitti, S. Quarteroni, R. Basili, and S. Manandhar. Exploiting syntactic and shallow semantic kernels for question answer classification. In ACL-07, pages 776--783, 2007.Google Scholar
- W. Wu, Z. Lu, and H. Li. Learning bilinear model for matching queries and documents. J. Mach. Learn. Res., 14(1):2519--2548, Jan. 2013. Google ScholarDigital Library
Index Terms
- SIGIR 2014 workshop on semantic matching in information retrieval
Recommendations
Exploration of query context for information retrieval
WWW '07: Proceedings of the 16th international conference on World Wide WebA number of existing information retrieval systems propose the notion of query context to combine the knowledge of query and user into retrieval to reveal the most exact description of user's information needs. In this paper we interpret query context ...
Information retrieval with concept-based pseudo-relevance feedback in MEDLINE
Although using domain specific knowledge sources for information retrieval yields more accurate results compared to pure keyword-based methods, more improvements can be achieved by considering both relations between concepts in an ontology and also their ...
Incorporating rich features to boost information retrieval performance
Research highlights We propose a regression-based re-ranking framework that can take into account rich features for boosting information retrieval (IR) performance. A set of salient features that may affect IR performance are investigated. Extensive ...
Comments