SenseLearner: Word Sense Disambiguation for All Words in Unrestricted Text

Description:

This paper describes SenseLearner, a minimally supervised word sense disambiguation system that attempts to disambiguate all content words in a text using WordNet senses.

Creator(s):
Creation Date: June 2005
Partner(s):
UNT College of Engineering
Collection(s):
UNT Scholarly Works
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Total Uses: 88
Past 30 days: 3
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Creator (Author):
Mihalcea, Rada, 1974-

University of North Texas

Creator (Author):
Csomai, Andras

University of North Texas

Publisher Info:
Place of Publication: [Stroudsburg, Pennsylvania]
Date(s):
  • Creation: June 2005
Description:

This paper describes SenseLearner, a minimally supervised word sense disambiguation system that attempts to disambiguate all content words in a text using WordNet senses.

Degree:
Note:

Abstract: This paper describes SenseLearner, a minimally supervised word sense disambiguation system that attempts to disambiguate all content words in a text using WordNet senses. The authors evaluate the accuracy of SenseLearner on several standard sense-annotated data sets, and show that it compares favorably with the best results reported during the recent SENSEVAL evaluations.

Physical Description:

4 p.

Language(s):
Subject(s):
Keyword(s): SenseLearner | sense annotated data sets | word sense disambiguation | WordNet senses
Source: Forty-Third Annual Meeting of the Association for Computational Linguistics (ACL), 2005, Ann Arbor, Michigan, United States
Contributor(s):
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • ARK: ark:/67531/metadc30975
Resource Type: Paper
Format: Text
Rights:
Access: Public