Text representations for text categorization: A case study in biomedical domain

Man Lan; Chew Lim Tan; Jian Su; Hwee Boon Low

doi:10.1109/IJCNN.2007.4371361

Text representations for text categorization: A case study in biomedical domain

Man Lan^*
, Chew Lim Tan
, Jian Su
, Hwee Boon Low

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

Abstract

In vector space model (VSM), textual documents are represented as vectors in the term space. Therefore, there are two issues in this representation, i.e. (1) what should a term be and (2) how to weight a term. This paper examined ways to represent text from the above two aspects to improve the performance of text categorization. Different representations have been evaluated using SVM on three biomedical corpora. The controlled experiments showed that the straightforward usage of named entities as terms in VSM does not show performance improvements over the bag-of-words representation. On the other hand, the term weighting method slightly improved the performance. However, to further improve the performance of text categorization, more advanced techniques and more effective usages of natural language processing for text representations appear needed.

Original language	English
Title of host publication	The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages	2557-2562
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2007.4371361
State	Published - 2007
Externally published	Yes
Event	2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States Duration: 12 Aug 2007 → 17 Aug 2007

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)	1098-7576

Conference

Conference	2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/Territory	United States
City	Orlando, FL
Period	12/08/07 → 17/08/07

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10.1109/IJCNN.2007.4371361

Cite this

Lan, M., Tan, C. L., Su, J., & Low, H. B. (2007). Text representations for text categorization: A case study in biomedical domain. In The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings (pp. 2557-2562). Article 4371361 (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/IJCNN.2007.4371361

@inproceedings{15923005fea64ccba62e7c3283900f5a,

title = "Text representations for text categorization: A case study in biomedical domain",

abstract = "In vector space model (VSM), textual documents are represented as vectors in the term space. Therefore, there are two issues in this representation, i.e. (1) what should a term be and (2) how to weight a term. This paper examined ways to represent text from the above two aspects to improve the performance of text categorization. Different representations have been evaluated using SVM on three biomedical corpora. The controlled experiments showed that the straightforward usage of named entities as terms in VSM does not show performance improvements over the bag-of-words representation. On the other hand, the term weighting method slightly improved the performance. However, to further improve the performance of text categorization, more advanced techniques and more effective usages of natural language processing for text representations appear needed.",

author = "Man Lan and Tan, \{Chew Lim\} and Jian Su and Low, \{Hwee Boon\}",

year = "2007",

doi = "10.1109/IJCNN.2007.4371361",

language = "英语",

isbn = "142441380X",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

pages = "2557--2562",

booktitle = "The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings",

note = "2007 International Joint Conference on Neural Networks, IJCNN 2007 ; Conference date: 12-08-2007 Through 17-08-2007",

}

Lan, M, Tan, CL, Su, J & Low, HB 2007, Text representations for text categorization: A case study in biomedical domain. in The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings., 4371361, IEEE International Conference on Neural Networks - Conference Proceedings, pp. 2557-2562, 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, United States, 12/08/07. https://doi.org/10.1109/IJCNN.2007.4371361

Text representations for text categorization: A case study in biomedical domain. / Lan, Man; Tan, Chew Lim; Su, Jian et al.
The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings. 2007. p. 2557-2562 4371361 (IEEE International Conference on Neural Networks - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In vector space model (VSM), textual documents are represented as vectors in the term space. Therefore, there are two issues in this representation, i.e. (1) what should a term be and (2) how to weight a term. This paper examined ways to represent text from the above two aspects to improve the performance of text categorization. Different representations have been evaluated using SVM on three biomedical corpora. The controlled experiments showed that the straightforward usage of named entities as terms in VSM does not show performance improvements over the bag-of-words representation. On the other hand, the term weighting method slightly improved the performance. However, to further improve the performance of text categorization, more advanced techniques and more effective usages of natural language processing for text representations appear needed.

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M3 - 会议稿件

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SN - 142441380X

SN - 9781424413805

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

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BT - The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings

Y2 - 12 August 2007 through 17 August 2007

ER -

Text representations for text categorization: A case study in biomedical domain

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