Integrating Knowledge for Subjectivity Sense Labeling

Description:

This paper discusses integrating knowledge for subjectivity sense labeling.

Creator(s):
Creation Date: May 2009
Partner(s):
UNT College of Engineering
Collection(s):
UNT Scholarly Works
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Total Uses: 41
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Creator (Author):
Gyamfi, Yaw

University of Pittsburgh

Creator (Author):
Wiebe, Janyce M.

University of Pittsburgh

Creator (Author):
Mihalcea, Rada, 1974-

University of North Texas

Creator (Author):
Akkaya, Cem

University of Pittsburgh

Date(s):
  • Creation: May 2009
Description:

This paper discusses integrating knowledge for subjectivity sense labeling.

Degree:
Note:

Abstract: This paper introduces an integrative approach to automatic word sense subjectivity annotation. We use features that exploit the hierarchical structure and domain information in lexical resources such as WordNet, as well as other types of features that measure the similarity of glosses and the overlap among sets of semantically related words. Integrated in a machine learning framework, the entire set of features is found to give better results than any individual type of feature.

Physical Description:

9 p.

Language(s):
Subject(s):
Keyword(s): word sense subjectivity | WordNet | subjectivity analysis | lexicons
Source: Association for Computational Linguistics. North American Chapter (NAACL) Conference, 2009, Boulder, Colorado, United States
Contributor(s):
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • ARK: ark:/67531/metadc31013
Resource Type: Paper
Format: Text
Rights:
Access: Public