Incorporating word embeddings into open directory project based large-scale classification

Kang Min Kim; Aliyeva Dinara; Byung Ju Choi; Sang Keun Lee

doi:10.1007/978-3-319-93037-4_30

Incorporating word embeddings into open directory project based large-scale classification

Kang Min Kim, Aliyeva Dinara, Byung Ju Choi, Sang Keun Lee

Korea University

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

4 Scopus citations

Abstract

Recently, implicit representation models, such as embedding or deep learning, have been successfully adopted to text classification task due to their outstanding performance. However, these approaches are limited to small- or moderate-scale text classification. Explicit representation models are often used in a large-scale text classification, like the Open Directory Project (ODP)-based text classification. However, the performance of these models is limited to the associated knowledge bases. In this paper, we incorporate word embeddings into the ODP-based large-scale classification. To this end, we first generate category vectors, which represent the semantics of ODP categories by jointly modeling word embeddings and the ODP-based text classification. We then propose a novel semantic similarity measure, which utilizes the category and word vectors obtained from the joint model. The evaluation results clearly show the efficacy of our methodology in large-scale text classification. The proposed scheme exhibits significant improvements of 10% and 28% in terms of macro-averaging F1-score and precision at k, respectively, over state-of-the-art techniques.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
Editors	Bao Ho, Dinh Phung, Geoffrey I. Webb, Vincent S. Tseng, Mohadeseh Ganji, Lida Rashidi
Publisher	Springer Verlag
Pages	376-388
Number of pages	13
ISBN (Print)	9783319930367
DOIs	https://doi.org/10.1007/978-3-319-93037-4_30
State	Published - 2018
Event	22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia Duration: 3 Jun 2018 → 6 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10938 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
Country/Territory	Australia
City	Melbourne
Period	3/06/18 → 6/06/18

Bibliographical note

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.

Keywords

Text classification
Word embeddings

Access to Document

10.1007/978-3-319-93037-4_30

Cite this

Kim, K. M., Dinara, A., Choi, B. J., & Lee, S. K. (2018). Incorporating word embeddings into open directory project based large-scale classification. In B. Ho, D. Phung, G. I. Webb, V. S. Tseng, M. Ganji, & L. Rashidi (Eds.), Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings (pp. 376-388). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10938 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-93037-4_30

Kim, Kang Min ; Dinara, Aliyeva ; Choi, Byung Ju et al. / Incorporating word embeddings into open directory project based large-scale classification. Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. editor / Bao Ho ; Dinh Phung ; Geoffrey I. Webb ; Vincent S. Tseng ; Mohadeseh Ganji ; Lida Rashidi. Springer Verlag, 2018. pp. 376-388 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Recently, implicit representation models, such as embedding or deep learning, have been successfully adopted to text classification task due to their outstanding performance. However, these approaches are limited to small- or moderate-scale text classification. Explicit representation models are often used in a large-scale text classification, like the Open Directory Project (ODP)-based text classification. However, the performance of these models is limited to the associated knowledge bases. In this paper, we incorporate word embeddings into the ODP-based large-scale classification. To this end, we first generate category vectors, which represent the semantics of ODP categories by jointly modeling word embeddings and the ODP-based text classification. We then propose a novel semantic similarity measure, which utilizes the category and word vectors obtained from the joint model. The evaluation results clearly show the efficacy of our methodology in large-scale text classification. The proposed scheme exhibits significant improvements of 10% and 28% in terms of macro-averaging F1-score and precision at k, respectively, over state-of-the-art techniques.",

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author = "Kim, {Kang Min} and Aliyeva Dinara and Choi, {Byung Ju} and Lee, {Sang Keun}",

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Kim, KM, Dinara, A, Choi, BJ & Lee, SK 2018, Incorporating word embeddings into open directory project based large-scale classification. in B Ho, D Phung, GI Webb, VS Tseng, M Ganji & L Rashidi (eds), Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10938 LNAI, Springer Verlag, pp. 376-388, 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, Australia, 3/06/18. https://doi.org/10.1007/978-3-319-93037-4_30

Incorporating word embeddings into open directory project based large-scale classification. / Kim, Kang Min; Dinara, Aliyeva; Choi, Byung Ju et al.
Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. ed. / Bao Ho; Dinh Phung; Geoffrey I. Webb; Vincent S. Tseng; Mohadeseh Ganji; Lida Rashidi. Springer Verlag, 2018. p. 376-388 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10938 LNAI).

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

TY - GEN

T1 - Incorporating word embeddings into open directory project based large-scale classification

AU - Kim, Kang Min

AU - Dinara, Aliyeva

AU - Choi, Byung Ju

AU - Lee, Sang Keun

PY - 2018

Y1 - 2018

N2 - Recently, implicit representation models, such as embedding or deep learning, have been successfully adopted to text classification task due to their outstanding performance. However, these approaches are limited to small- or moderate-scale text classification. Explicit representation models are often used in a large-scale text classification, like the Open Directory Project (ODP)-based text classification. However, the performance of these models is limited to the associated knowledge bases. In this paper, we incorporate word embeddings into the ODP-based large-scale classification. To this end, we first generate category vectors, which represent the semantics of ODP categories by jointly modeling word embeddings and the ODP-based text classification. We then propose a novel semantic similarity measure, which utilizes the category and word vectors obtained from the joint model. The evaluation results clearly show the efficacy of our methodology in large-scale text classification. The proposed scheme exhibits significant improvements of 10% and 28% in terms of macro-averaging F1-score and precision at k, respectively, over state-of-the-art techniques.

AB - Recently, implicit representation models, such as embedding or deep learning, have been successfully adopted to text classification task due to their outstanding performance. However, these approaches are limited to small- or moderate-scale text classification. Explicit representation models are often used in a large-scale text classification, like the Open Directory Project (ODP)-based text classification. However, the performance of these models is limited to the associated knowledge bases. In this paper, we incorporate word embeddings into the ODP-based large-scale classification. To this end, we first generate category vectors, which represent the semantics of ODP categories by jointly modeling word embeddings and the ODP-based text classification. We then propose a novel semantic similarity measure, which utilizes the category and word vectors obtained from the joint model. The evaluation results clearly show the efficacy of our methodology in large-scale text classification. The proposed scheme exhibits significant improvements of 10% and 28% in terms of macro-averaging F1-score and precision at k, respectively, over state-of-the-art techniques.

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BT - Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings

A2 - Ho, Bao

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A2 - Webb, Geoffrey I.

A2 - Tseng, Vincent S.

A2 - Ganji, Mohadeseh

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PB - Springer Verlag

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Kim KM, Dinara A, Choi BJ, Lee SK. Incorporating word embeddings into open directory project based large-scale classification. In Ho B, Phung D, Webb GI, Tseng VS, Ganji M, Rashidi L, editors, Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. Springer Verlag. 2018. p. 376-388. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-93037-4_30

Incorporating word embeddings into open directory project based large-scale classification

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