This article presents a measure of semantic similarityinanis-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their e#ectiveness. 1. Introduction Evaluating semantic relatedness using network representations is a problem with a long history in arti#cial intelligence and psychology, dating back to the spreading activation approach of Quillian #1968# and Collins and Loftus #1975#. Semantic similarity represents a special case of semantic relatedness: for example, cars and gasoline would seem to be more closely related than, say, cars and bicycles, but the latter pair are certainly more similar. Rada et al. #Rada, Mili, Bicknell, & Blett...

This paper describes an experimental com- parison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text.

The imact of NLP on information retrieval tasks has largely been one of promise rather than substance. While there are exceptions to this as some of the chapters in the present volume demonstrate, for the most part NLP and information retrieval have only recently started to dovetail together. In this chapter we will present a pr'ecis of our experiments in information retrieval using NLP which have had mixed successover the last few years. We introduce the respective roles of NLP and IR and then we summarise our early experiments on using syntactic analysis to derive term dependencies and structured representations of term-term relationships. We then re-thought the role that NLP could have for IR tasks and decided to concentrate our efforts onto using NLP resources rather than NLP tools in information retrieval and our more recent experiments in this area in which we use WordNet are summarised. Finally we present our conclusions and the status of our work. 1 2. Introduction The develo...

Automatic query expansion has been known to be the most important method in overcoming the word mismatch problem in information retrieval. Thesauri have long been used by many researchers as a tool for query expansion. However only one type of thesaurus has generally been used. In this paper we analyze the characteristics of different thesaurus types and propose a method to combine them for query expansion. Experiments using the TREC collection proved the effectiveness of our method over those using one type of thesaurus.