research-article

Improved information gain feature selection method for Chinese text classification based on word embedding

Authors:

Xianchun ZouAuthors Info & Claims

ICSCA '17: Proceedings of the 6th International Conference on Software and Computer Applications

Pages 72 - 76

https://doi.org/10.1145/3056662.3056671

Published: 26 February 2017 Publication History

Abstract

Feature selection is a very important part of text categorization, which can reduce the dimensionality of the text representation by vector space model (VSM), and can avoid the problem of "curse of dimensionality". Information gain (IG) feature selection algorithm is one of the most effective feature selection algorithms, but it is easy to filter out the characteristic words which have a low IG score but have a strong ability of text type identification. Meanwhile, these words are often very similar to the words of high IG score. Aiming at this defect, we propose an improved feature selection method which uses word embedding to calculate the most similar words to the current dictionary selected by IG algorithm and expand the dictionary with these words under certain regulations. Finally we achieve good experimental results in Sogou Chinese text classification corpus and Fudan Chinese text classification corpus.

References

[1]

Ren, Y. G. 2012. Information-gain-based text feature selection method. Computer Science.

[2]

Yang, Y., & Pedersen, J. O. (1998). A Comparative Study on Feature Selection in Text Categorization. Fourteenth International Conference on Machine Learning (Vol.4, pp.412--420). Morgan Kaufmann Publishers Inc.

Digital Library

[3]

Uğuz, H. 2011. A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. 24(7), 1024--1032.

Digital Library

[4]

Javed, K., Maruf, S. and Babri, H. A. 2015. A two-stage markov blanket based feature selection algorithm for text classification. Neurocomputing, 157, 91--104.

[5]

Wang, G.Y. 2012. Cloud Model and Granular Computing.Science Press.

[6]

Li, H. R. 2012. Research on Term Weighting Approach Based on Information Gain and Entropy. CHONGQING UNIVERSITY, 46--47

[7]

Mikolov, T., Chen, K., Corrado, G. and Dean, J. 2013. Efficient estimation of word representations in vector space. Computer Science. arXiv preprint arXiv:1301.3781,2013. DOI= http://arXiv:1301.3781

[8]

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. and Dean, J. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111--3119). DOI= http://arXiv:1310.4546

Digital Library

[9]

http://www.sogou.com/labs/dl/c.html.

[10]

http://www.nlpir.org/download/tc-corpus-answer.rar

[11]

Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research, 3(2), 1289--1305.

Digital Library

[12]

Mikolov, T., Yih, W. T. and Zweig, G. 2013. Linguistic regularities in continuous space word representations. In HLT-NAACL.

[13]

http://finance.sina.com.cn/stock/roll/2016-05-08/doc-ifxryhhh1757728.shtml.

[14]

http://ictclas.nlpir.org/nlpir/.

[15]

Hao, X. and Zhang, L. 2003. A Feature selection Algorithm based on class distribution of word for text classification. symposium of Search Engine and Web Mining. Peking University.

[16]

Kusner M. J., Sun, Y., Kolkin, N. I. and Weinberger, K. Q. 2015. From word embeddings to document distances. 957--966.

Digital Library

[17]

Sun, M.S., Chen, X. X., Zhang, K. X., Guo, Z. P. and Liu, Z. Y. 2016. THULAC: An Efficient Lexical Analyzer for Chinese.

[18]

https://code.google.com/p/word2vec/.

[19]

http://radimrehurek.com/gensim/index.html

Cited By

Thirumoorthy KBritto J(2024)A feature selection model for document classification using Tom and Jerry Optimization algorithmMultimedia Tools and Applications10.1007/s11042-023-15828-683:4(10273-10295)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15828-6
Mou SDu PCheng Z(2021)A brain-inspired information processing algorithm and its application in text classificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114828177:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114828
Zhang XLiu JCao BShi M(2021)Web service classification based on information gain theory and bidirectional long short‐term memory with attention mechanismConcurrency and Computation: Practice and Experience10.1002/cpe.620233:13Online publication date: 18-Mar-2021
https://doi.org/10.1002/cpe.6202
Show More Cited By

Index Terms

Improved information gain feature selection method for Chinese text classification based on word embedding
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection

Recommendations

Multi-prototype Morpheme Embedding for Text Classification
SMA 2020: The 9th International Conference on Smart Media and Applications

Representing a word into a continuous space, also known as a word vector, has been successful in various NLP tasks. The word-based embedding has two problems; one is the out-of-vocabulary problem and the other is does not take into account the context ...
An improved global feature selection scheme for text classification

An improved global feature selection scheme is proposed for text classification.It is an ensemble method combining the power of two filter-based methods.The new method combines a global and a one-sided local feature selection method.By incorporating ...
LFWE: Linguistic Feature Based Word Embedding for Hindi Fake News Detection
It is essential for research communities to investigate ways for authenticating news. The use of linguistic feature based analysis to automatically detect false news is gaining popularity among the scientific community. However, such techniques are ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICSCA '17: Proceedings of the 6th International Conference on Software and Computer Applications

February 2017

339 pages

ISBN:9781450348577

DOI:10.1145/3056662

Conference Chairs:
Vitaliy Mezhuyev
Universiti Malaysia Pahang, Malaysia
,
Luigi Benedicenti
University of Regina, Canada

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 February 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICSCA 2017

ICSCA 2017: 2017 6th International Conference on Software and Computer Applications

February 26 - 28, 2017

Bangkok, Thailand

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
312
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Thirumoorthy KBritto J(2024)A feature selection model for document classification using Tom and Jerry Optimization algorithmMultimedia Tools and Applications10.1007/s11042-023-15828-683:4(10273-10295)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15828-6
Mou SDu PCheng Z(2021)A brain-inspired information processing algorithm and its application in text classificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114828177:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114828
Zhang XLiu JCao BShi M(2021)Web service classification based on information gain theory and bidirectional long short‐term memory with attention mechanismConcurrency and Computation: Practice and Experience10.1002/cpe.620233:13Online publication date: 18-Mar-2021
https://doi.org/10.1002/cpe.6202
Erlic ZReel PReel SAmar LPecori ALarsen CTetti MPamporaki CPrehn CAdamski JPrejbisz ACeccato FScaroni CKroiss MDennedy MDeinum JLangton KMulatero PReincke MLenzini LGimenez-Roqueplo AAssié GBlanchard AZennaro MJefferson EBeuschlein F(2020)Targeted Metabolomics as a Tool in Discriminating Endocrine From Primary HypertensionThe Journal of Clinical Endocrinology & Metabolism10.1210/clinem/dgaa954Online publication date: 31-Dec-2020
https://doi.org/10.1210/clinem/dgaa954
Zhang XLiu JCao BXiao QWen Y(2019)Web Service Discovery Based on Information Gain Theory and BiLSTM with Attention MechanismMethionine Dependence of Cancer and Aging10.1007/978-3-030-12981-1_45(643-658)Online publication date: 7-Feb-2019
https://doi.org/10.1007/978-3-030-12981-1_45
Wang YHuang JLiu YTu LLiu L(2018)Inter-Category Distribution Enhanced Feature Extraction for Efficient Text ClassificationBig Data – BigData 201810.1007/978-3-319-94301-5_2(17-25)Online publication date: 21-Jun-2018
https://doi.org/10.1007/978-3-319-94301-5_2

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents