Text Categorization for Improved Priors of Word Meaning

Distributions of the senses of words are often highly skewed. This fact is exploited by word sense disambiguation (WSD) systems which back off to the predominant (most frequent) sense of a word when contextual clues are not strong enough. The topic domain of a document has a strong influence on the sense distribution of words. Unfortunately, it is not feasible to produce large manually sense-annotated corpora for every domain of interest. Previous experiments have shown that unsupervised estimation of the predominant sense of certain words using corpora whose domain has been determined by hand outperforms estimates based on domain-independent text for a subset of words and even outperforms the estimates based on counting occurrences in an annotated corpus. In this paper we address the question of whether we can automatically produce domain-specific corpora which could be used to acquire predominant senses appropriate for specific domains. We collect the corpora by automatically cla...

• Type: Conference
• Year: 2007
• Where: CICLING
• Authors: Rob Koeling, Diana McCarthy, John Carroll
• Keywords: CICLING 2007, Domain-specific Predominant Sense, Natural Language Processing, Predominant Sense, Word Sense Disambiguation