8,878 research outputs found
Word sense disambiguation and information retrieval
It has often been thought that word sense ambiguity is a cause of poor performance in Information Retrieval
(IR) systems. The belief is that if ambiguous words can be correctly disambiguated, IR performance will
increase. However, recent research into the application of a word sense disambiguator to an IR system failed
to show any performance increase. From these results it has become clear that more basic research is needed
to investigate the relationship between sense ambiguity, disambiguation, and IR.
Using a technique that introduces additional sense ambiguity into a collection, this paper presents research
that goes beyond previous work in this field to reveal the influence that ambiguity and disambiguation have
on a probabilistic IR system. We conclude that word sense ambiguity is only problematic to an IR system
when it is retrieving from very short queries. In addition we argue that if a word sense disambiguator is to
be of any use to an IR system, the disambiguator must be able to resolve word senses to a high degree of
accuracy
Word sense disambiguation and information retrieval
It has often been thought that word sense ambiguity is a cause of poor performance in Information Retrieval
(IR) systems. The belief is that if ambiguous words can be correctly disambiguated, IR performance will
increase. However, recent research into the application of a word sense disambiguator to an IR system failed
to show any performance increase. From these results it has become clear that more basic research is needed
to investigate the relationship between sense ambiguity, disambiguation, and IR.
Using a technique that introduces additional sense ambiguity into a collection, this paper presents research
that goes beyond previous work in this field to reveal the influence that ambiguity and disambiguation have
on a probabilistic IR system. We conclude that word sense ambiguity is only problematic to an IR system
when it is retrieving from very short queries. In addition we argue that if a word sense disambiguator is to
be of any use to an IR system, the disambiguator must be able to resolve word senses to a high degree of
accuracy
Distinguishing Word Senses in Untagged Text
This paper describes an experimental comparison of three unsupervised
learning algorithms that distinguish the sense of an ambiguous word in untagged
text. The methods described in this paper, McQuitty's similarity analysis,
Ward's minimum-variance method, and the EM algorithm, assign each instance of
an ambiguous word to a known sense definition based solely on the values of
automatically identifiable features in text. These methods and feature sets are
found to be more successful in disambiguating nouns rather than adjectives or
verbs. Overall, the most accurate of these procedures is McQuitty's similarity
analysis in combination with a high dimensional feature set.Comment: 11 pages, latex, uses aclap.st
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