Text categorization using hybrid (mined) terms

C. K.P. Wong; Wing Pong Robert Luk; K. F. Wong; K. L. Kwok

doi:10.1145/355214.355252

Text categorization using hybrid (mined) terms

C. K.P. Wong, Wing Pong Robert Luk, K. F. Wong, K. L. Kwok

The Hong Kong Polytechnic University

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

2 Citations (Scopus)

Abstract

2000 ACM. This paper evaluated text categorization using charactes, bigrams, words and hybrid terms. These terms were also augmented with mined terms. Classifiers using hybrid terms did not achieve better classification performance. The use of data mining techniques to add new terms to the dictionary improves the performance of character-based classifiers. Our naive comparison between the Pat-tree classifier and our best classifier shows that the Pat-tree classifier has the best precision (77%) and our best classifier has the best recall (72%) and the lowest storage requirement (13%).

Original language	English
Title of host publication	Proceedings of the 5th international Workshop on Information Retrieval with Asian Languages, IRAL 2000
Publisher	Association for Computing Machinery, Inc
Pages	217-218
Number of pages	2
ISBN (Electronic)	1581133006, 9781581133004
DOIs	https://doi.org/10.1145/355214.355252
Publication status	Published - 1 Nov 2000
Event	5th International Workshop on Information Retrieval with Asian Languages, IRAL 2000 - Hong Kong, Hong Kong Duration: 30 Sept 2000 → 1 Oct 2000

Conference

Conference	5th International Workshop on Information Retrieval with Asian Languages, IRAL 2000
Country/Territory	Hong Kong
City	Hong Kong
Period	30/09/00 → 1/10/00

Keywords

Data mining
Evaluation
Text categorization

ASJC Scopus subject areas

Computer Science Applications
Information Systems

More information

10.1145/355214.355252

Cite this

@inproceedings{899745c3f64c4e7f9b4d997356c83b80,

title = "Text categorization using hybrid (mined) terms",

abstract = "2000 ACM. This paper evaluated text categorization using charactes, bigrams, words and hybrid terms. These terms were also augmented with mined terms. Classifiers using hybrid terms did not achieve better classification performance. The use of data mining techniques to add new terms to the dictionary improves the performance of character-based classifiers. Our naive comparison between the Pat-tree classifier and our best classifier shows that the Pat-tree classifier has the best precision (77%) and our best classifier has the best recall (72%) and the lowest storage requirement (13%).",

keywords = "Data mining, Evaluation, Text categorization",

author = "Wong, {C. K.P.} and Luk, {Wing Pong Robert} and Wong, {K. F.} and Kwok, {K. L.}",

year = "2000",

month = nov,

day = "1",

doi = "10.1145/355214.355252",

language = "English",

pages = "217--218",

booktitle = "Proceedings of the 5th international Workshop on Information Retrieval with Asian Languages, IRAL 2000",

publisher = "Association for Computing Machinery, Inc",

note = "5th International Workshop on Information Retrieval with Asian Languages, IRAL 2000 ; Conference date: 30-09-2000 Through 01-10-2000",

}

Wong, CKP, Luk, WPR, Wong, KF & Kwok, KL 2000, Text categorization using hybrid (mined) terms. in Proceedings of the 5th international Workshop on Information Retrieval with Asian Languages, IRAL 2000. Association for Computing Machinery, Inc, pp. 217-218, 5th International Workshop on Information Retrieval with Asian Languages, IRAL 2000, Hong Kong, Hong Kong, 30/09/00. https://doi.org/10.1145/355214.355252

Text categorization using hybrid (mined) terms. / Wong, C. K.P.; Luk, Wing Pong Robert; Wong, K. F. et al.
Proceedings of the 5th international Workshop on Information Retrieval with Asian Languages, IRAL 2000. Association for Computing Machinery, Inc, 2000. p. 217-218.

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

TY - GEN

T1 - Text categorization using hybrid (mined) terms

AU - Wong, C. K.P.

AU - Luk, Wing Pong Robert

AU - Wong, K. F.

AU - Kwok, K. L.

PY - 2000/11/1

Y1 - 2000/11/1

N2 - 2000 ACM. This paper evaluated text categorization using charactes, bigrams, words and hybrid terms. These terms were also augmented with mined terms. Classifiers using hybrid terms did not achieve better classification performance. The use of data mining techniques to add new terms to the dictionary improves the performance of character-based classifiers. Our naive comparison between the Pat-tree classifier and our best classifier shows that the Pat-tree classifier has the best precision (77%) and our best classifier has the best recall (72%) and the lowest storage requirement (13%).

AB - 2000 ACM. This paper evaluated text categorization using charactes, bigrams, words and hybrid terms. These terms were also augmented with mined terms. Classifiers using hybrid terms did not achieve better classification performance. The use of data mining techniques to add new terms to the dictionary improves the performance of character-based classifiers. Our naive comparison between the Pat-tree classifier and our best classifier shows that the Pat-tree classifier has the best precision (77%) and our best classifier has the best recall (72%) and the lowest storage requirement (13%).

KW - Data mining

KW - Evaluation

KW - Text categorization

UR - http://www.scopus.com/inward/record.url?scp=74549133403&partnerID=8YFLogxK

U2 - 10.1145/355214.355252

DO - 10.1145/355214.355252

M3 - Conference article published in proceeding or book

SP - 217

EP - 218

BT - Proceedings of the 5th international Workshop on Information Retrieval with Asian Languages, IRAL 2000

PB - Association for Computing Machinery, Inc

T2 - 5th International Workshop on Information Retrieval with Asian Languages, IRAL 2000

Y2 - 30 September 2000 through 1 October 2000

ER -

Text categorization using hybrid (mined) terms

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this