Machine learning approaches for chinese shallow parsers

Qin Lu; Jing Zhou; Rui Feng Xu

Machine learning approaches for chinese shallow parsers

Qin Lu
, Jing Zhou
, Rui Feng Xu

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

2 Citations (Scopus)

Abstract

In this paper, we present two machine-learning algorithms, namely, transformation-based error-driven learning (TEL) and memory-based learning (MBL) to improve the performance of a Chinese shallow parser. The Algorithm not only can handle nested chunking data, but also different phrase types (e.g. NP, VP, S etc.). Results show that TEL can achieve better recall rate, yet MBL is less sensitive to nesting and requires much less computation.

Original language	English
Title of host publication	International Conference on Machine Learning and Cybernetics
Pages	2309-2314
Number of pages	6
Volume	4
Publication status	Published - 1 Dec 2003
Event	2003 International Conference on Machine Learning and Cybernetics - Xi'an, China Duration: 2 Nov 2003 → 5 Nov 2003

Conference

Conference	2003 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Xi'an
Period	2/11/03 → 5/11/03

Keywords

Machine learning algorithms
Natural language processing
Shallow parsers Introduction

ASJC Scopus subject areas

Artificial Intelligence

Cite this

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title = "Machine learning approaches for chinese shallow parsers",

abstract = "In this paper, we present two machine-learning algorithms, namely, transformation-based error-driven learning (TEL) and memory-based learning (MBL) to improve the performance of a Chinese shallow parser. The Algorithm not only can handle nested chunking data, but also different phrase types (e.g. NP, VP, S etc.). Results show that TEL can achieve better recall rate, yet MBL is less sensitive to nesting and requires much less computation.",

keywords = "Machine learning algorithms, Natural language processing, Shallow parsers Introduction",

author = "Qin Lu and Jing Zhou and Xu, \{Rui Feng\}",

year = "2003",

month = dec,

day = "1",

language = "English",

isbn = "0780378652",

volume = "4",

pages = "2309--2314",

booktitle = "International Conference on Machine Learning and Cybernetics",

note = "2003 International Conference on Machine Learning and Cybernetics ; Conference date: 02-11-2003 Through 05-11-2003",

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TY - GEN

T1 - Machine learning approaches for chinese shallow parsers

AU - Lu, Qin

AU - Zhou, Jing

AU - Xu, Rui Feng

PY - 2003/12/1

Y1 - 2003/12/1

N2 - In this paper, we present two machine-learning algorithms, namely, transformation-based error-driven learning (TEL) and memory-based learning (MBL) to improve the performance of a Chinese shallow parser. The Algorithm not only can handle nested chunking data, but also different phrase types (e.g. NP, VP, S etc.). Results show that TEL can achieve better recall rate, yet MBL is less sensitive to nesting and requires much less computation.

AB - In this paper, we present two machine-learning algorithms, namely, transformation-based error-driven learning (TEL) and memory-based learning (MBL) to improve the performance of a Chinese shallow parser. The Algorithm not only can handle nested chunking data, but also different phrase types (e.g. NP, VP, S etc.). Results show that TEL can achieve better recall rate, yet MBL is less sensitive to nesting and requires much less computation.

KW - Machine learning algorithms

KW - Natural language processing

KW - Shallow parsers Introduction

UR - https://www.scopus.com/pages/publications/1542315441

M3 - Conference article published in proceeding or book

SN - 0780378652

SN - 9780780378650

VL - 4

SP - 2309

EP - 2314

BT - International Conference on Machine Learning and Cybernetics

T2 - 2003 International Conference on Machine Learning and Cybernetics

Y2 - 2 November 2003 through 5 November 2003

ER -

Machine learning approaches for chinese shallow parsers

Abstract

Conference

Keywords

ASJC Scopus subject areas

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Cite this