An algorithm combining statistics-based and rules-based for chunk identification of Chinese sentences

Wang Rongbo; Zheru Chi

An algorithm combining statistics-based and rules-based for chunk identification of Chinese sentences

Wang Rongbo, Zheru Chi

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

Natural language processing (NLP) is a very hot research domain. One important branch of it is sentence analysis, including Chinese sentence analysis. However, currently, no mature deep analysis theories and techniques are available. An alternative way is to perform shallow parsing on sentences which is very popular in the domain. The chunk identification is a fundamental task for shallow parsing. The purpose of this paper is to characterize a chunk boundary parsing algorithm, using a statistical method combining adjustment rules, which serves as a supplement to traditional statistics-based parsing methods. The experimental results show that the model works well on the small dataset. It will contribute to the sequent processes like chunk tagging and chunk collocation extraction under other topics etc.

Original language	English
Title of host publication	PACLIC 20 - Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation
Pages	446-451
Number of pages	6
Publication status	Published - 1 Dec 2006
Event	20th Pacific Asia Conference on Language, Information and Computation, PACLIC 20 - Wuhan, China Duration: 1 Nov 2006 → 3 Nov 2006

Conference

Conference	20th Pacific Asia Conference on Language, Information and Computation, PACLIC 20
Country/Territory	China
City	Wuhan
Period	1/11/06 → 3/11/06

ASJC Scopus subject areas

Language and Linguistics
Computer Science (miscellaneous)

Cite this

@inproceedings{e9e15a92ca28477faeee47c2f8d0219b,

title = "An algorithm combining statistics-based and rules-based for chunk identification of Chinese sentences",

abstract = "Natural language processing (NLP) is a very hot research domain. One important branch of it is sentence analysis, including Chinese sentence analysis. However, currently, no mature deep analysis theories and techniques are available. An alternative way is to perform shallow parsing on sentences which is very popular in the domain. The chunk identification is a fundamental task for shallow parsing. The purpose of this paper is to characterize a chunk boundary parsing algorithm, using a statistical method combining adjustment rules, which serves as a supplement to traditional statistics-based parsing methods. The experimental results show that the model works well on the small dataset. It will contribute to the sequent processes like chunk tagging and chunk collocation extraction under other topics etc.",

author = "Wang Rongbo and Zheru Chi",

year = "2006",

month = dec,

day = "1",

language = "English",

pages = "446--451",

booktitle = "PACLIC 20 - Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation",

note = "20th Pacific Asia Conference on Language, Information and Computation, PACLIC 20 ; Conference date: 01-11-2006 Through 03-11-2006",

}

An algorithm combining statistics-based and rules-based for chunk identification of Chinese sentences. / Rongbo, Wang; Chi, Zheru.
PACLIC 20 - Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation. 2006. p. 446-451.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Chi, Zheru

PY - 2006/12/1

Y1 - 2006/12/1

N2 - Natural language processing (NLP) is a very hot research domain. One important branch of it is sentence analysis, including Chinese sentence analysis. However, currently, no mature deep analysis theories and techniques are available. An alternative way is to perform shallow parsing on sentences which is very popular in the domain. The chunk identification is a fundamental task for shallow parsing. The purpose of this paper is to characterize a chunk boundary parsing algorithm, using a statistical method combining adjustment rules, which serves as a supplement to traditional statistics-based parsing methods. The experimental results show that the model works well on the small dataset. It will contribute to the sequent processes like chunk tagging and chunk collocation extraction under other topics etc.

AB - Natural language processing (NLP) is a very hot research domain. One important branch of it is sentence analysis, including Chinese sentence analysis. However, currently, no mature deep analysis theories and techniques are available. An alternative way is to perform shallow parsing on sentences which is very popular in the domain. The chunk identification is a fundamental task for shallow parsing. The purpose of this paper is to characterize a chunk boundary parsing algorithm, using a statistical method combining adjustment rules, which serves as a supplement to traditional statistics-based parsing methods. The experimental results show that the model works well on the small dataset. It will contribute to the sequent processes like chunk tagging and chunk collocation extraction under other topics etc.

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M3 - Conference article published in proceeding or book

SP - 446

EP - 451

BT - PACLIC 20 - Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation

T2 - 20th Pacific Asia Conference on Language, Information and Computation, PACLIC 20

Y2 - 1 November 2006 through 3 November 2006

ER -

An algorithm combining statistics-based and rules-based for chunk identification of Chinese sentences

Abstract

Conference

ASJC Scopus subject areas

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