Cummins, Ronan and O’Riordan, Colm (2009) Learning in a pairwise term-term proximity framework for information retrieval. In: Proceedings of the 32nd international ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, USA, pp. 251-258. ISBN 978-1605584836 (doi:https://doi.org/10.1145/1571941.1571986)

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Abstract

Traditional ad hoc retrieval models do not take into account the closeness or proximity of terms. Document scores in these models are primarily based on the occurrences or nonoccurrences of query-terms considered independently of each other. Intuitively, documents in which query-terms occur closer together should be ranked higher than documents in which the query-terms appear far apart.
This paper outlines several term-term proximity measures and develops an intuitive framework in which they can be used to fully model the proximity of all query-terms for a particular topic. As useful proximity functions may be constructed from many proximity measures, we use a learning approach to combine proximity measures to develop a useful proximity function in the framework. An evaluation of the best proximity functions show that there is a significant improvement over the baseline ad hoc retrieval model and over other more recent methods that employ the use of single proximity measures.

Item Type:	Conference Proceedings
Title of Proceedings:	Proceedings of the 32nd international ACM SIGIR Conference on Research and Development in Information Retrieval
Additional Information:	[1] First published: 2009. [2] Published as: Cummins, Ronan and O’Riordan, Colm (2009) Learning in a pairwise term-term proximity framework for information retrieval. In: Proceedings of the 32nd international ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, USA, pp. 251-258. [3] This paper was first presented at SIGIR '09, The 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval held from 19-23 July 2009 in Boston, Massachusetts, USA. The paper was given on 21 July 2012 within Session 4B: Learning to Rank I. [4] SIGIR (Special Interest Group on Information Retrieval) is a Special Interest Group of the Association of Computing Machinery (ACM).
Uncontrolled Keywords:	retrieval models, search process, experimentation, performance, information retrieval, learning to rank, proximity
Subjects:	Q Science > QA Mathematics > QA76 Computer software Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Pre-2014 Departments:	School of Computing & Mathematical Sciences
Related URLs:	Publisher Organisation Organisation Organisation
Last Modified:	27 Sep 2019 14:29
URI:	http://gala.gre.ac.uk/id/eprint/10098

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