NDLI: Word sense disambiguation in queries

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Word sense disambiguation in queries

Content Provider	ACM Digital Library
Author	Meng, Weiyi Liu, Shuang Yu, Clement
Abstract	This paper presents a new approach to determine the senses of words in queries by using WordNet. In our approach, noun phrases in a query are determined first. For each word in the query, information associated with it, including its synonyms, hyponyms, hypernyms, definitions of its synonyms and hyponyms, and its domains, can be used for word sense disambiguation. By comparing these pieces of information associated with the words which form a phrase, it may be possible to assign senses to these words. If the above disambiguation fails, then other query words, if exist, are used, by going through exactly the same process. If the sense of a query word cannot be determined in this manner, then a guess of the sense of the word is made, if the guess has at least 50% chance of being correct. If no sense of the word has 50% or higher chance of being used, then we apply a Web search to assist in the word sense disambiguation process. Experimental results show that our approach has 100% applicability and 90% accuracy on the most recent robust track of TREC collection of 250 queries. We combine this disambiguation algorithm to our retrieval system to examine the effect of word sense disambiguation in text retrieval. Experimental results show that the disambiguation algorithm together with other components of our retrieval system yield a result which is 13.7% above that produced by the same system but without the disambiguation, and 9.2% above that produced by using Lesk's algorithm. Our retrieval effectiveness is 7% better than the best reported result in the literature.
Starting Page	525
Ending Page	532
Page Count	8
File Format	PDF
ISBN	1595931406
DOI	10.1145/1099554.1099696
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Word sense disambiguation Information retrieval Wordnet
Content Type	Text
Resource Type	Article

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