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

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.
Volume	2018-July
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

Other

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

ASJC Scopus subject areas

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

Access to Document

10.1109/FUZZ-IEEE.2018.8491626

Fingerprint Dive into the research topics of 'Utilizing probase in open directory project-based text classification'. Together they form a unique fingerprint.

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 (Vol. 2018-July). [8491626] 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; Lee, Sang-Geun.

2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings. Vol. 2018-July Institute of Electrical and Electronics Engineers Inc., 2018. 8491626.

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. vol. 2018-July, 8491626, 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

@inproceedings{b00f602489e3421b9c7b780cd4237140,

title = "Utilizing probase in open directory project-based text classification",

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.",

author = "Jun, {So Young} and Dinara Aliyeva and Lee, {Ji Min} and Sang-Geun Lee",

year = "2018",

month = oct,

day = "12",

doi = "10.1109/FUZZ-IEEE.2018.8491626",

language = "English",

volume = "2018-July",

booktitle = "2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

}

TY - GEN

T1 - Utilizing probase in open directory project-based text classification

AU - Jun, So Young

AU - Aliyeva, Dinara

AU - Lee, Ji Min

AU - Lee, Sang-Geun

PY - 2018/10/12

Y1 - 2018/10/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85060477192&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060477192&partnerID=8YFLogxK

U2 - 10.1109/FUZZ-IEEE.2018.8491626

DO - 10.1109/FUZZ-IEEE.2018.8491626

M3 - Conference contribution

AN - SCOPUS:85060477192

VL - 2018-July

BT - 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018

Y2 - 8 July 2018 through 13 July 2018

ER -