One Homonym per Translation

The study of homonymy is vital to resolving fundamental problems in lexical semantics. In this paper, we propose four hypotheses that characterize the unique behavior of homonyms in the context of translations, discourses, collocations, and sense clusters. We present a new annotated homonym resource that allows us to test our hypotheses on existing WSD resources. The results of the experiments provide strong empirical evidence for the hypotheses. This study represents a step towards a computational method for distinguishing between homonymy and polysemy, and constructing a definitive inventory of coarse-grained senses.Comment: 8 pages, including reference

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

Sense Tagging: Semantic Tagging with a Lexicon

Sense tagging, the automatic assignment of the appropriate sense from some lexicon to each of the words in a text, is a specialised instance of the general problem of semantic tagging by category or type. We discuss which recent word sense disambiguation algorithms are appropriate for sense tagging. It is our belief that sense tagging can be carried out effectively by combining several simple, independent, methods and we include the design of such a tagger. A prototype of this system has been implemented, correctly tagging 86% of polysemous word tokens in a small test set, providing evidence that our hypothesis is correct.Comment: 6 pages, uses aclap LaTeX style file. Also in Proceedings of the SIGLEX Workshop "Tagging Text with Lexical Semantics

Word sense disambiguation criteria: a systematic study

This article describes the results of a systematic in-depth study of the criteria used for word sense disambiguation. Our study is based on 60 target words: 20 nouns, 20 adjectives and 20 verbs. Our results are not always in line with some practices in the field. For example, we show that omitting non-content words decreases performance and that bigrams yield better results than unigrams