Lexical Data Augmentation for Text Classification in Deep Learning

Rong Xiang; Emmanuele Chersoni; Yunfei Long; Qin Lu; Chu Ren Huang

doi:10.1007/978-3-030-47358-7_53

Lexical Data Augmentation for Text Classification in Deep Learning

Rong Xiang, Emmanuele Chersoni, Yunfei Long, Qin Lu, Chu Ren Huang

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

5 Citations (Scopus)

Abstract

This paper presents our work on using part-of-speech focused lexical substitution for data augmentation (PLSDA) to enhance the prediction capabilities and the performance of deep learning models. This paper explains how PLSDA uses part-of-speech information to identify words and make use of different augmentation strategies to find semantically related substitutions to generate new instances for training. Evaluations of PLSDA is conducted on a variety of datasets across different text classification tasks. When PLSDA is applied to four deep learning models, results show that classifiers trained with PLSDA achieve 1.3% accuracy improvement on average.

Original language	English
Title of host publication	Advances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Proceedings
Editors	Cyril Goutte, Xiaodan Zhu
Publisher	Springer
Pages	521-527
Number of pages	7
ISBN (Print)	9783030473570
DOIs	https://doi.org/10.1007/978-3-030-47358-7_53
Publication status	Published - 1 Jan 2020
Event	33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020 - Ottawa, Canada Duration: 13 May 2020 → 15 May 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12109 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020
Country/Territory	Canada
City	Ottawa
Period	13/05/20 → 15/05/20

Keywords

Data augmentation
Deep learning
Lexical data augmentation
Text classification

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

More information

10.1007/978-3-030-47358-7_53

http://hdl.handle.net/10397/90116

Cite this

Xiang, R., Chersoni, E., Long, Y., Lu, Q., & Huang, C. R. (2020). Lexical Data Augmentation for Text Classification in Deep Learning. In C. Goutte, & X. Zhu (Eds.), Advances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Proceedings (pp. 521-527). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12109 LNAI). Springer. https://doi.org/10.1007/978-3-030-47358-7_53

Xiang, Rong ; Chersoni, Emmanuele ; Long, Yunfei et al. / Lexical Data Augmentation for Text Classification in Deep Learning. Advances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Proceedings. editor / Cyril Goutte ; Xiaodan Zhu. Springer, 2020. pp. 521-527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Lexical Data Augmentation for Text Classification in Deep Learning",

abstract = "This paper presents our work on using part-of-speech focused lexical substitution for data augmentation (PLSDA) to enhance the prediction capabilities and the performance of deep learning models. This paper explains how PLSDA uses part-of-speech information to identify words and make use of different augmentation strategies to find semantically related substitutions to generate new instances for training. Evaluations of PLSDA is conducted on a variety of datasets across different text classification tasks. When PLSDA is applied to four deep learning models, results show that classifiers trained with PLSDA achieve 1.3% accuracy improvement on average.",

keywords = "Data augmentation, Deep learning, Lexical data augmentation, Text classification",

author = "Rong Xiang and Emmanuele Chersoni and Yunfei Long and Qin Lu and Huang, {Chu Ren}",

note = "Funding Information: Acknowledgements. We acknowledge the research grants from Hong Kong Polytechnic University (PolyU RTVU) and GRF grant (CERG PolyU 15211/14E, PolyU 152006/16E). Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020 ; Conference date: 13-05-2020 Through 15-05-2020",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Xiang, R, Chersoni, E, Long, Y, Lu, Q & Huang, CR 2020, Lexical Data Augmentation for Text Classification in Deep Learning. in C Goutte & X Zhu (eds), Advances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12109 LNAI, Springer, pp. 521-527, 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Ottawa, Canada, 13/05/20. https://doi.org/10.1007/978-3-030-47358-7_53

Lexical Data Augmentation for Text Classification in Deep Learning. / Xiang, Rong; Chersoni, Emmanuele; Long, Yunfei et al.
Advances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Proceedings. ed. / Cyril Goutte; Xiaodan Zhu. Springer, 2020. p. 521-527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12109 LNAI).

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

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AU - Chersoni, Emmanuele

AU - Long, Yunfei

AU - Lu, Qin

AU - Huang, Chu Ren

N1 - Funding Information: Acknowledgements. We acknowledge the research grants from Hong Kong Polytechnic University (PolyU RTVU) and GRF grant (CERG PolyU 15211/14E, PolyU 152006/16E). Publisher Copyright: © Springer Nature Switzerland AG 2020.

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Y1 - 2020/1/1

N2 - This paper presents our work on using part-of-speech focused lexical substitution for data augmentation (PLSDA) to enhance the prediction capabilities and the performance of deep learning models. This paper explains how PLSDA uses part-of-speech information to identify words and make use of different augmentation strategies to find semantically related substitutions to generate new instances for training. Evaluations of PLSDA is conducted on a variety of datasets across different text classification tasks. When PLSDA is applied to four deep learning models, results show that classifiers trained with PLSDA achieve 1.3% accuracy improvement on average.

AB - This paper presents our work on using part-of-speech focused lexical substitution for data augmentation (PLSDA) to enhance the prediction capabilities and the performance of deep learning models. This paper explains how PLSDA uses part-of-speech information to identify words and make use of different augmentation strategies to find semantically related substitutions to generate new instances for training. Evaluations of PLSDA is conducted on a variety of datasets across different text classification tasks. When PLSDA is applied to four deep learning models, results show that classifiers trained with PLSDA achieve 1.3% accuracy improvement on average.

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Xiang R, Chersoni E, Long Y, Lu Q, Huang CR. Lexical Data Augmentation for Text Classification in Deep Learning. In Goutte C, Zhu X, editors, Advances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Proceedings. Springer. 2020. p. 521-527. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-47358-7_53

Lexical Data Augmentation for Text Classification in Deep Learning

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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