Word sense disambiguation in queries

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.

What makes a query difficult?

“…Theoretically, the topic aspects might have been found by analyzing the query facet, for instance, by using disambiguation methods described in [10]. However, when using the TREC data we assume that the relevant documents for a topic cover all the topic relevant aspects.…”

Analysis of Word Sense Disambiguation-Based Information Retrieval

“…That said, QE must be controlled in order to carefully choose the concepts to be added to the original query, otherwise the results can be disappointing [3][15]. In [11] and [12], the authors obtained positive results by expanding queries using WSD, but the effect of the use of WSD and QE are not quantified in isolation. In fact, even though the main objective of their study was to evaluate the performance of WSD in IR, they should have examined the accuracy of their disambiguation method in isolation, so that they could quantify its effect when used in their IR experiments.…”

Sense-Based Information Retrieval Using Fuzzy Logic and Swarm Intelligence

“…Their experiments exhibited that the use of the heterogeneous thesauri gives better retrieval results than just using one type of thesaurus. The work in [27] used the WordNet and the web search to determine the sense of expansion terms, the query expansion was carried using pseudo feedback. More recently the work in [28] introduced a new method of query expansion based on relevance feedback and latent semantic analysis.…”