Experiments on Using Semantic Distances Between Words in Image Caption Retrieval.

Alan F. Smeaton, Ian Quigley: Experiments on Using Semantic Distances Between Words in Image Caption Retrieval. SIGIR 1996: 174-180

@inproceedings{DBLP:conf/sigir/SmeatonQ96,
  author    = {Alan F. Smeaton and
               Ian Quigley},
  editor    = {Hans-Peter Frei and
               Donna Harman and
               Peter Sch{\"a}uble and
               Ross Wilkinson},
  title     = {Experiments on Using Semantic Distances Between Words in Image
               Caption Retrieval},
  booktitle = {Proceedings of the 19th Annual International ACM SIGIR Conference
               on Research and Development in Information Retrieval, SIGIR'96,
               August 18-22, 1996, Zurich, Switzerland (Special Issue of the
               SIGIR Forum)},
  publisher = {ACM},
  year      = {1996},
  isbn      = {0-89791-792-8},
  pages     = {174-180},
  ee        = {db/conf/sigir/SmeatonQ96.html},
  crossref  = {DBLP:conf/sigir/96},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

BibTeX

Abstract

Traditional approaches to information retrieval are based upon representing a user's query as a bag of query terms and a document as a bag of index terms and computing a degree of similarity between the two based on the overlap or number of query terms in common between them. Our long-term approach to IR applications is based upon precomputing semantically-based word-word similarities, work which is described elsewhere, and using these as part of the document-query similarity measure. A basic premise of our word-to-word similarity measure is that the input to this computation is the correct or intended word sense but in information retrieval applications, automatic and accurate word sense disambiguation remains an unsolved problem. In this paper we describe our first successful application of these ideas to an information retrieval application, specifically the indexing and retrieval of captions describing the content of images. We have hand-captioned 2714 images and to circumvent, for the time being, the problems raised by word sense disambiguation, we manually disambiguated polysemous words in captions. We have also built a Collection of 60 queries and for each, determined relevance assessments. Using this environment we were able to run experiments in which we varied how the query-caption similarity measure used our pre-computed word-word semantic distances. Our experiments, reported in the paper, show significant improvement for this environment over the more traditional approaches to information retrieval.

Copyright © 1996 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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Printed Edition

Hans-Peter Frei, Donna Harman, Peter Schäuble, Ross Wilkinson (Eds.): Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'96, August 18-22, 1996, Zurich, Switzerland (Special Issue of the SIGIR Forum). ACM 1996, ISBN 0-89791-792-8
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Referenced by

Tao Guan, Miao Liu, Lawrence V. Saxton: Structure-Based Queries over the World Wide Web. ER 1998: 107-120

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