Bringing bag-of-phrases to ODP-based text classification

Haeyong Shin; Byung Gul Ryu; Woo Jong Ryu; Geunjae Lee; Sang-Geun Lee

doi:10.1109/BIGCOMP.2016.7425975

Bringing bag-of-phrases to ODP-based text classification

Haeyong Shin
, Byung Gul Ryu
, Woo Jong Ryu
, Geunjae Lee
, Sang-Geun Lee

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

1 Citation (Scopus)

Abstract

The Open Directory Project (ODP) is a large scale, high quality and publicly available web directory. Many studies and real-world applications build on an ODP-based classifier. However, existing approaches use traditional bag-of-words representation of text to develop an ODP-based classifier and words alone do not always provide atomic units of semantic meaning. In this paper, we propose a novel framework to better understand the semantic meaning of text by bringing bag-of-phrases to ODP-based text classification. The proposed method employs a syntactic tree to extract phrases from ODP and applies a phrase selection method to alleviate the high dimensionality problem of bag-of-phrases. The conducted evaluation results demonstrate that our approach outperforms the state-of-the-art methods in classification performance.

Original language	English
Title of host publication	2016 International Conference on Big Data and Smart Computing, BigComp 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	485-488
Number of pages	4
ISBN (Electronic)	9781467387965
DOIs	https://doi.org/10.1109/BIGCOMP.2016.7425975
Publication status	Published - 2016 Mar 3
Event	International Conference on Big Data and Smart Computing, BigComp 2016 - Hong Kong, China Duration: 2016 Jan 18 → 2016 Jan 20

Publication series

Name	2016 International Conference on Big Data and Smart Computing, BigComp 2016

Other

Other	International Conference on Big Data and Smart Computing, BigComp 2016
Country/Territory	China
City	Hong Kong
Period	16/1/18 → 16/1/20

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

open directory project
syntactic structure
text classification
text mining

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Information Systems and Management

Access to Document

10.1109/BIGCOMP.2016.7425975

Cite this

Shin, H., Ryu, B. G., Ryu, W. J., Lee, G., & Lee, S.-G. (2016). Bringing bag-of-phrases to ODP-based text classification. In 2016 International Conference on Big Data and Smart Computing, BigComp 2016 (pp. 485-488). Article 7425975 (2016 International Conference on Big Data and Smart Computing, BigComp 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIGCOMP.2016.7425975

Bringing bag-of-phrases to ODP-based text classification. / Shin, Haeyong; Ryu, Byung Gul; Ryu, Woo Jong et al.
2016 International Conference on Big Data and Smart Computing, BigComp 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 485-488 7425975 (2016 International Conference on Big Data and Smart Computing, BigComp 2016).

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

Shin, H, Ryu, BG, Ryu, WJ, Lee, G & Lee, S-G 2016, Bringing bag-of-phrases to ODP-based text classification. in 2016 International Conference on Big Data and Smart Computing, BigComp 2016., 7425975, 2016 International Conference on Big Data and Smart Computing, BigComp 2016, Institute of Electrical and Electronics Engineers Inc., pp. 485-488, International Conference on Big Data and Smart Computing, BigComp 2016, Hong Kong, China, 16/1/18. https://doi.org/10.1109/BIGCOMP.2016.7425975

Shin H, Ryu BG, Ryu WJ, Lee G, Lee SG. Bringing bag-of-phrases to ODP-based text classification. In 2016 International Conference on Big Data and Smart Computing, BigComp 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 485-488. 7425975. (2016 International Conference on Big Data and Smart Computing, BigComp 2016). doi: 10.1109/BIGCOMP.2016.7425975

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abstract = "The Open Directory Project (ODP) is a large scale, high quality and publicly available web directory. Many studies and real-world applications build on an ODP-based classifier. However, existing approaches use traditional bag-of-words representation of text to develop an ODP-based classifier and words alone do not always provide atomic units of semantic meaning. In this paper, we propose a novel framework to better understand the semantic meaning of text by bringing bag-of-phrases to ODP-based text classification. The proposed method employs a syntactic tree to extract phrases from ODP and applies a phrase selection method to alleviate the high dimensionality problem of bag-of-phrases. The conducted evaluation results demonstrate that our approach outperforms the state-of-the-art methods in classification performance.",

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author = "Haeyong Shin and Ryu, \{Byung Gul\} and Ryu, \{Woo Jong\} and Geunjae Lee and Sang-Geun Lee",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; International Conference on Big Data and Smart Computing, BigComp 2016 ; Conference date: 18-01-2016 Through 20-01-2016",

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AB - The Open Directory Project (ODP) is a large scale, high quality and publicly available web directory. Many studies and real-world applications build on an ODP-based classifier. However, existing approaches use traditional bag-of-words representation of text to develop an ODP-based classifier and words alone do not always provide atomic units of semantic meaning. In this paper, we propose a novel framework to better understand the semantic meaning of text by bringing bag-of-phrases to ODP-based text classification. The proposed method employs a syntactic tree to extract phrases from ODP and applies a phrase selection method to alleviate the high dimensionality problem of bag-of-phrases. The conducted evaluation results demonstrate that our approach outperforms the state-of-the-art methods in classification performance.

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Bringing bag-of-phrases to ODP-based text classification

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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