SenseLearner: Minimally Supervised Word Sense Disambiguation for All Words in Open Text

Description:

This paper introduces SenseLearner - a minimally supervised sense tagger that attempts to disambiguate all content words in a text using the sense from WordNet. SenseLearner participated in the SENSEVAL-3 English all words task, and achieved an average accuracy of 64.6%.

Creator(s):
Creation Date: 2004
Partner(s):
UNT College of Engineering
Collection(s):
UNT Scholarly Works
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Total Uses: 86
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Creator (Author):
Mihalcea, Rada, 1974-

University of North Texas

Creator (Author):
Faruque, Ehsanul

University of North Texas

Publisher Info:
Place of Publication: [Stroudsburg, Pennsylvania]
Date(s):
  • Creation: 2004
Description:

This paper introduces SenseLearner - a minimally supervised sense tagger that attempts to disambiguate all content words in a text using the sense from WordNet. SenseLearner participated in the SENSEVAL-3 English all words task, and achieved an average accuracy of 64.6%.

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Physical Description:

4 p.

Language(s):
Subject(s):
Keyword(s): SenseLearner | natural language processing | sense tags | word sense disambiguation
Source: Association for Computational Linguistics (ACL)/SIGLEX Senseval-3 Conference, 2004, Barcelona, Spain
Contributor(s):
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • ARK: ark:/67531/metadc30961
Resource Type: Paper
Format: Text
Rights:
Access: Public