Chapter 5: Knowledge-Based Methods for WSD

Rada Mihalcea

Abstract

This chapter provides an overview of research to date in knowledge-based word sense disambiguation. It outlines the main knowledge-intensive methods devised so far for automatic sense tagging: 1) methods using contextual overlap with respect to dictionary definitions, 2) methods based on similarity measures computed on semantic networks, 3) selectional preferences as a means of constraining the possible meanings of words in a given context, and 4) heuristic-based methods that rely on properties of human language including the most frequent sense, one sense per discourse, and one sense per collocation.

Links

WordNet::Similarity Perl Module

Contents

5.1 Introduction. 107

5.2 Lesk algorithm.. 108

5.2.1 Variations of the Lesk algorithm.. 110

Simulated annealing. 110

Simplified Lesk algorithm.. 111

Augmented semantic spaces. 113

Summary. 113

5.3 Semantic similarity. 114

5.3.1 Measures of semantic similarity. 114

5.3.2 Using semantic similarity within a local context 117

5.3.3 Using semantic similarity within a global context 118

5.4 Selectional preferences. 119

5.4.1 Preliminaries: Learning word-to-word relations. 120

5.4.2 Learning selectional preferences. 120

5.4.3 Using selectional preferences. 122

5.4 Heuristics for word sense disambiguation. 123

5.5.1 Most frequent sense. 123

5.5.2 One sense per discourse. 124

5.5.3 One sense per collocation. 124

5.6 Knowledge-based methods at Senseval-2. 125

5.7 Conclusions. 126

References. 127