Semantic Role Labeling Using Maximum Entropy

Kwok Cheung Lan; Kei Shiu Ho; Wing Pong Robert Luk; Hong Va Leong

Semantic Role Labeling Using Maximum Entropy

Kwok Cheung Lan
, Kei Shiu Ho
, Wing Pong Robert Luk
, Hong Va Leong

Research output: Journal article publication › Journal article › Academic research › peer-review

Abstract

In this paper, semantic role labeling is addressed. We formulate the problem as a classification task, in which the words of a sentence are assigned to semantic role classes using a classifier. The maximum entropy approach is applied to train the classifier, by using a large real corpus annotated with argument structures.

Original language	English
Pages (from-to)	954-961
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3314
Publication status	Published - 1 Dec 2004

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Cite this

@article{902db63110eb494f99289969765f7bef,

title = "Semantic Role Labeling Using Maximum Entropy",

abstract = "In this paper, semantic role labeling is addressed. We formulate the problem as a classification task, in which the words of a sentence are assigned to semantic role classes using a classifier. The maximum entropy approach is applied to train the classifier, by using a large real corpus annotated with argument structures.",

author = "Lan, \{Kwok Cheung\} and Ho, \{Kei Shiu\} and Luk, \{Wing Pong Robert\} and Leong, \{Hong Va\}",

year = "2004",

month = dec,

day = "1",

language = "English",

volume = "3314",

pages = "954--961",

journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

}

Semantic Role Labeling Using Maximum Entropy. / Lan, Kwok Cheung; Ho, Kei Shiu; Luk, Wing Pong Robert et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3314, 01.12.2004, p. 954-961.

Research output: Journal article publication › Journal article › Academic research › peer-review

TY - JOUR

T1 - Semantic Role Labeling Using Maximum Entropy

AU - Lan, Kwok Cheung

AU - Ho, Kei Shiu

AU - Luk, Wing Pong Robert

AU - Leong, Hong Va

PY - 2004/12/1

Y1 - 2004/12/1

N2 - In this paper, semantic role labeling is addressed. We formulate the problem as a classification task, in which the words of a sentence are assigned to semantic role classes using a classifier. The maximum entropy approach is applied to train the classifier, by using a large real corpus annotated with argument structures.

AB - In this paper, semantic role labeling is addressed. We formulate the problem as a classification task, in which the words of a sentence are assigned to semantic role classes using a classifier. The maximum entropy approach is applied to train the classifier, by using a large real corpus annotated with argument structures.

UR - https://www.scopus.com/pages/publications/35048819577

M3 - Journal article

SN - 0302-9743

VL - 3314

SP - 954

EP - 961

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Semantic Role Labeling Using Maximum Entropy

Abstract

ASJC Scopus subject areas

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