A Minimally Supervised Word Sense Disambiguation Algorithm Using Syntactic Dependencies and Semantic Generalizations

Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institution or a river shore. Finding the correct meaning of a word in a particular context is a task known as word sense disambiguation (WSD), which is essential for many natural language processing applications such as machine translation, information retrieval, and others. While most current WSD methods try to disambiguate a small number of words for which enough annotated examples are available, the method proposed in this thesis attempts to address all words in unrestricted text. The method is based on constraints imposed by syntactic dependencies and concept generalizations drawn from an external dictionary. The method was tested on standard benchmarks as used during the SENSEVAL-2 and SENSEVAL-3 WSD international evaluation exercises, and was found to be competitive.

Grantor dc:publisher

University of North Texas

Year dc:date

2005

Author dc:creator

• Faruque, Md. Ehsanul

Contributors dc:contributor

• Mihalcea, Rada, 1974-
• Tarau, Paul
• Chen, Jiangping

• Computational linguistics.
• Discourse analysis.
• Semantics -- Data processing.
• word sense disambiguation
• minimally supervised algorithm
• semantic generalizations
• machine translation
• syntactic dependencies

Statement dc:rights

• Public
• Copyright
• Faruque, Md. Ehsanul
• Copyright is held by the author, unless otherwise noted. All rights reserved.

Language dc:language

English

Identifier

oclc: 68903162
https://digital.library.unt.edu/ark:/67531/metadc4969/
ark: ark:/67531/metadc4969

OAI identifier oai:identifier

info:ark/67531/metadc4969