2012
Journal Articles

Adrian-Gabriel Chifu; Radu-Tudor Ionescu
Word Sense Disambiguation to Improve Precision for Ambiguous Queries Journal Article
In: Central European Journal of Computer Science, vol. 2, no. 4, pp. 398–411, 2012.
Abstract | Links | BibTeX | Tags: Ambiguous Queries, Difficult Queries, Document Clustering, Fusion Functions, Information Retrieval, Naïve Bayes Classification, Word Sense Disambiguation
@article{chifu2012word,
title = {Word Sense Disambiguation to Improve Precision for Ambiguous Queries},
author = {Adrian-Gabriel Chifu and Radu-Tudor Ionescu},
url = {https://www.researchgate.net/publication/257910091_Word_sense_disambiguation_to_improve_precision_for_ambiguous_queries},
year = {2012},
date = {2012-12-28},
urldate = {2012-01-01},
journal = {Central European Journal of Computer Science},
volume = {2},
number = {4},
pages = {398--411},
publisher = {Springer},
abstract = {Success in Information Retrieval (IR) depends on many variables. Several interdisciplinary approaches try to improve the quality of the results obtained by an IR system. In this paper we propose a new way of using word sense disambiguation (WSD) in IR. The method we develop is based on Naïve Bayes classification and can be used both as a filtering and as a re-ranking technique. We show on the TREC ad-hoc collection that WSD is useful in the case of queries which are difficult due to sense ambiguity. Our interest regards improving the precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30), respectively, for such lowest precision queries.},
keywords = {Ambiguous Queries, Difficult Queries, Document Clustering, Fusion Functions, Information Retrieval, Naïve Bayes Classification, Word Sense Disambiguation},
pubstate = {published},
tppubtype = {article}
}
Success in Information Retrieval (IR) depends on many variables. Several interdisciplinary approaches try to improve the quality of the results obtained by an IR system. In this paper we propose a new way of using word sense disambiguation (WSD) in IR. The method we develop is based on Naïve Bayes classification and can be used both as a filtering and as a re-ranking technique. We show on the TREC ad-hoc collection that WSD is useful in the case of queries which are difficult due to sense ambiguity. Our interest regards improving the precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30), respectively, for such lowest precision queries.
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