Word Sense Disambiguation using Optimised Combinations of Knowledge Sources (1998)

Content Provider	CiteSeerX
Author	Wilks, Yorick Stevenson, Mark
Description	In: Proceedings of COLING-ACL'98
Abstract	Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowledge source. We describe a system which performs word sense disambiguation on all content words in free text by combining different knowledge sources: semantic preferences, dictionary definitions and subject /domain codes along with part-of-speech tags, optimised by means of a learning algorithm. We also describe the creation of a new sense tagged corpus by combining existing resources. Tested accuracy of our approach on this corpus exceeds 92%, demonstrating the viability of all-word disambiguation rather than restricting oneself to a small sample. 1 Introduction This paper describes a system that integrates a number of partial sources of information to perform word sense disambiguation (WSD) of content words in general text at a high level of accuracy. The methodology and evaluation of WSD are somewhat different from those of other NLP modules, and one can distinguish three aspects of ...
File Format	PDF
Publisher Date	1998-01-01
Access Restriction	Open
Subject Keyword	Partial Source Nlp Module General Text Dictionary Definition Different Knowledge Source Subject Domain Code Content Word Word Sense Disambiguation Algorithm Optimised Combination Learning Algorithm Knowledge Source All-word Disambiguation Semantic Preference Free Text Lexical Knowledge Source Part-of-speech Tag Word Sense Disambiguation New Sense Small Sample
Content Type	Text
Resource Type	Proceeding Article