Utilizing probase in open directory project-based text classification

So Young Jun; Dinara Aliyeva; Ji Min Lee; Sang-Geun Lee

doi:10.1109/FUZZ-IEEE.2018.8491626

Utilizing probase in open directory project-based text classification

So Young Jun, Dinara Aliyeva, Ji Min Lee, Sang-Geun Lee

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

2 Citations (Scopus)

Abstract

Open Directory Project (ODP) has been successfully utilized in text classification due to its representation ability of various categories. However, ODP includes a limited number of entities, which play an important role in classification tasks. In this paper, we enrich the semantics of ODP categories with Probase entities. To effectively incorporate Probase entities in ODP categories, we first represent each ODP category and Probase entity in terms of concepts. Next, we measure the semantic relevance between an ODP category and a Probase entity based on the concept vector. Finally, we use Probase entity to enrich the semantics of the ODP categories. Our experimental results show that the proposed methodology exhibits a significant improvement over state-of-the-art techniques in the ODP-based text classification.

Original language	English
Title of host publication	2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060207
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2018.8491626
Publication status	Published - 2018 Oct 12
Event	2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Rio de Janeiro, Brazil Duration: 2018 Jul 8 → 2018 Jul 13

Publication series

Name	IEEE International Conference on Fuzzy Systems
Volume	2018-July
ISSN (Print)	1098-7584

Other

Other	2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	18/7/8 → 18/7/13

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

Access to Document

10.1109/FUZZ-IEEE.2018.8491626

Cite this

Jun, S. Y., Aliyeva, D., Lee, J. M., & Lee, S.-G. (2018). Utilizing probase in open directory project-based text classification. In 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings Article 8491626 (IEEE International Conference on Fuzzy Systems; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2018.8491626

Utilizing probase in open directory project-based text classification. / Jun, So Young; Aliyeva, Dinara; Lee, Ji Min et al.
2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8491626 (IEEE International Conference on Fuzzy Systems; Vol. 2018-July).

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

Jun, SY, Aliyeva, D, Lee, JM & Lee, S-G 2018, Utilizing probase in open directory project-based text classification. in 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings., 8491626, IEEE International Conference on Fuzzy Systems, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018, Rio de Janeiro, Brazil, 18/7/8. https://doi.org/10.1109/FUZZ-IEEE.2018.8491626

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abstract = "Open Directory Project (ODP) has been successfully utilized in text classification due to its representation ability of various categories. However, ODP includes a limited number of entities, which play an important role in classification tasks. In this paper, we enrich the semantics of ODP categories with Probase entities. To effectively incorporate Probase entities in ODP categories, we first represent each ODP category and Probase entity in terms of concepts. Next, we measure the semantic relevance between an ODP category and a Probase entity based on the concept vector. Finally, we use Probase entity to enrich the semantics of the ODP categories. Our experimental results show that the proposed methodology exhibits a significant improvement over state-of-the-art techniques in the ODP-based text classification.",

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Utilizing probase in open directory project-based text classification

Abstract

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