PNNL: A supervised maximum entropy approach to Word Sense Disambiguation

Stephen Tratz; Antonio Sanfilippo; Michelle Gregory; Alan Chappell; Christian Posse; Paul Whitney

PNNL: A supervised maximum entropy approach to Word Sense Disambiguation

Stephen Tratz
, Antonio Sanfilippo
, Michelle Gregory
, Alan Chappell
, Christian Posse
, Paul Whitney

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, we described the PNNL Word Sense Disambiguation system as applied to the English all-word task in SemEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for dimension reduction. The rich feature set combined with a Maximum Entropy classifier produces results that are significantly better than baseline and are the highest Fscore for the fined-grained English allwords subtask of SemEval.

Original language	English
Pages	264-267
Number of pages	4
State	Published - 2007
Externally published	Yes
Event	4th International Workshop on Semantic Evaluations, SemEval 2007 in conjunction with the Association for Computational Linguistics, ACL 2007 - Prague, Czech Republic Duration: Jun 23 2007 → Jun 24 2007

Conference

Conference	4th International Workshop on Semantic Evaluations, SemEval 2007 in conjunction with the Association for Computational Linguistics, ACL 2007
Country/Territory	Czech Republic
City	Prague
Period	06/23/07 → 06/24/07

Cite this

@conference{8a072d4bd34f468aa62ff80023abd3bc,

title = "PNNL: A supervised maximum entropy approach to Word Sense Disambiguation",

abstract = "In this paper, we described the PNNL Word Sense Disambiguation system as applied to the English all-word task in SemEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for dimension reduction. The rich feature set combined with a Maximum Entropy classifier produces results that are significantly better than baseline and are the highest Fscore for the fined-grained English allwords subtask of SemEval.",

author = "Stephen Tratz and Antonio Sanfilippo and Michelle Gregory and Alan Chappell and Christian Posse and Paul Whitney",

note = "Publisher Copyright: {\textcopyright} 2007 Association for Computational Linguistics.; 4th International Workshop on Semantic Evaluations, SemEval 2007 in conjunction with the Association for Computational Linguistics, ACL 2007 ; Conference date: 23-06-2007 Through 24-06-2007",

year = "2007",

language = "Ingl{\'e}s",

pages = "264--267",

}

Tratz, S, Sanfilippo, A, Gregory, M, Chappell, A, Posse, C & Whitney, P 2007, 'PNNL: A supervised maximum entropy approach to Word Sense Disambiguation', Paper presented at 4th International Workshop on Semantic Evaluations, SemEval 2007 in conjunction with the Association for Computational Linguistics, ACL 2007, Prague, Czech Republic, 06/23/07 - 06/24/07 pp. 264-267.

PNNL: A supervised maximum entropy approach to Word Sense Disambiguation. / Tratz, Stephen; Sanfilippo, Antonio; Gregory, Michelle et al.
2007. 264-267 Paper presented at 4th International Workshop on Semantic Evaluations, SemEval 2007 in conjunction with the Association for Computational Linguistics, ACL 2007, Prague, Czech Republic.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - PNNL

T2 - 4th International Workshop on Semantic Evaluations, SemEval 2007 in conjunction with the Association for Computational Linguistics, ACL 2007

AU - Tratz, Stephen

AU - Sanfilippo, Antonio

AU - Gregory, Michelle

AU - Chappell, Alan

AU - Posse, Christian

AU - Whitney, Paul

PY - 2007

Y1 - 2007

N2 - In this paper, we described the PNNL Word Sense Disambiguation system as applied to the English all-word task in SemEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for dimension reduction. The rich feature set combined with a Maximum Entropy classifier produces results that are significantly better than baseline and are the highest Fscore for the fined-grained English allwords subtask of SemEval.

AB - In this paper, we described the PNNL Word Sense Disambiguation system as applied to the English all-word task in SemEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for dimension reduction. The rich feature set combined with a Maximum Entropy classifier produces results that are significantly better than baseline and are the highest Fscore for the fined-grained English allwords subtask of SemEval.

UR - https://www.scopus.com/pages/publications/85036476413

M3 - Artículo

AN - SCOPUS:85036476413

SP - 264

EP - 267

Y2 - 23 June 2007 through 24 June 2007

ER -

PNNL: A supervised maximum entropy approach to Word Sense Disambiguation

Abstract

Conference

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