Rule-based Word Clustering for Text Classification

Hui Han; Eren Manavoglu; C. Lee Giles; Hongyuan Zha

doi:10.1145/860500.860543

Rule-based Word Clustering for Text Classification

Hui Han
, Eren Manavoglu
, C. Lee Giles
, Hongyuan Zha

Research output: Contribution to journal › Conference article › peer-review

15 Scopus citations

Abstract

This paper introduces a rule-based, context-dependent word clustering method, with the rules derived from various domain databases and the word text orthographic properties. Besides significant dimensionality reduction, our experiments show that such rule-based word clustering improves by 8% the overall accuracy of extracting bibliographic fields from references, and by 18.32% on average the class-specific performance on the line classification of document headers.

Original language	English (US)
Pages (from-to)	445-446
Number of pages	2
Journal	SIGIR Forum (ACM Special Interest Group on Information Retrieval)
Issue number	SPEC. ISS.
DOIs	https://doi.org/10.1145/860500.860543
State	Published - 2003
Event	Proceedings of the Twenty-Sixth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2003 - Toronto, Ont., Canada Duration: Jul 28 2003 → Aug 1 2003

All Science Journal Classification (ASJC) codes

Management Information Systems
Hardware and Architecture

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10.1145/860500.860543

Cite this

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title = "Rule-based Word Clustering for Text Classification",

abstract = "This paper introduces a rule-based, context-dependent word clustering method, with the rules derived from various domain databases and the word text orthographic properties. Besides significant dimensionality reduction, our experiments show that such rule-based word clustering improves by 8\% the overall accuracy of extracting bibliographic fields from references, and by 18.32\% on average the class-specific performance on the line classification of document headers.",

author = "Hui Han and Eren Manavoglu and Giles, \{C. Lee\} and Hongyuan Zha",

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note = "Proceedings of the Twenty-Sixth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2003 ; Conference date: 28-07-2003 Through 01-08-2003",

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T1 - Rule-based Word Clustering for Text Classification

AU - Han, Hui

AU - Manavoglu, Eren

AU - Giles, C. Lee

AU - Zha, Hongyuan

PY - 2003

Y1 - 2003

N2 - This paper introduces a rule-based, context-dependent word clustering method, with the rules derived from various domain databases and the word text orthographic properties. Besides significant dimensionality reduction, our experiments show that such rule-based word clustering improves by 8% the overall accuracy of extracting bibliographic fields from references, and by 18.32% on average the class-specific performance on the line classification of document headers.

AB - This paper introduces a rule-based, context-dependent word clustering method, with the rules derived from various domain databases and the word text orthographic properties. Besides significant dimensionality reduction, our experiments show that such rule-based word clustering improves by 8% the overall accuracy of extracting bibliographic fields from references, and by 18.32% on average the class-specific performance on the line classification of document headers.

UR - https://www.scopus.com/pages/publications/1542317626

UR - https://www.scopus.com/pages/publications/1542317626#tab=citedBy

U2 - 10.1145/860500.860543

DO - 10.1145/860500.860543

M3 - Conference article

AN - SCOPUS:1542317626

SN - 0163-5840

SP - 445

EP - 446

JO - SIGIR Forum (ACM Special Interest Group on Information Retrieval)

JF - SIGIR Forum (ACM Special Interest Group on Information Retrieval)

IS - SPEC. ISS.

T2 - Proceedings of the Twenty-Sixth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2003

Y2 - 28 July 2003 through 1 August 2003

ER -

Rule-based Word Clustering for Text Classification

Abstract

All Science Journal Classification (ASJC) codes

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