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SUBJECTIVITY WORD SENSE DISAMBIGUATION: A METHOD FOR SENSE-AWARE SUBJECTIVITY ANALYSIS

AKKAYA, CEM (2014) SUBJECTIVITY WORD SENSE DISAMBIGUATION: A METHOD FOR SENSE-AWARE SUBJECTIVITY ANALYSIS. Doctoral Dissertation, University of Pittsburgh.

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Abstract

Subjectivity lexicons have been invaluable resources in subjectivity analysis and their creation has been an important topic. Many systems rely on these lexicons. For any subjectivity analysis system, which relies on a subjectivity lexicon, subjectivity sense ambiguity is a serious problem. Such systems will be misled by the presence of subjectivity clues used with objective senses called false hits.
We believe that any type of subjectivity analysis system relying on lexicons will benefit from a sense-aware approach. We think sense-aware subjectivity analysis has been neglected mostly because of the concerns related to word sense disambiguation (WSD), the problem of automatically determining which sense of a word is activated by the use of the word in a particular context according to a sense-inventory. Although WSD is the perfect tool for sense-aware classification, trust in traditional fine-grained WSD as an enabling technology is not high due to previous mostly unsuccessful results.
In this thesis, we investigate feasible and practical methods to avoid these false hits via sense-aware analysis. We define a new coarse-grained WSD task capturing the right semantic granularity specific to subjectivity analysis.


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Details

Item Type: University of Pittsburgh ETD
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
AKKAYA, CEMcea18@pitt.eduCEA18
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWiebe, Janycewiebe@cs.pitt.eduJMW106
Committee MemberLitman, Dianelitman@cs.pitt.eduDLITMAN
Committee MemberHauskrecht, Milosmilos@cs.pitt.eduMILOS
Committee MemberLee, Adam J.adamlee@cs.pitt.eduADAMLEE
Date: 26 May 2014
Date Type: Publication
Defense Date: 20 June 2013
Approval Date: 26 May 2014
Submission Date: 24 June 2013
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 143
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Subjectivity Analysis, Sentiment Analysis, Opinion Analysis, Subjectivity Word Sense Disambiguation, Word Sense Disambiguation, Constrained Clustering, Semi-supervised Clustering, Natural Language Processing, Distributional Semantic Models, Amazon Mechanical Turk
Date Deposited: 26 May 2014 22:51
Last Modified: 15 Nov 2016 14:18
URI: http://d-scholarship.pitt.edu/id/eprint/20967

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