Semantically enriching text representation model for document clustering

Han Joon Kim; Kee Joo Hong; Jae Young Chang

doi:10.1145/2695664.2696055

Semantically enriching text representation model for document clustering

Han Joon Kim, Kee Joo Hong, Jae Young Chang

School of Electrical and Computer Engineering

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

7 Scopus citations

Abstract

This paper presents a novel text space model that represents textual documents for document clustering, which contains the 'concept' space independently of the 'document' and 'term' spaces. The text model described here represents documents as matrices (i.e., 2ⁿd-order tensors), and a document corpus is represented as a 3^rd-order tensor. For this, it is necessary to produce the concept vector for each term that occurs in a given document, which is related to word sense disambiguation. As an external knowledge source for concept weighting, we employ the Wikipedia encyclopedia.

Original language	English
Title of host publication	2015 Symposium on Applied Computing, SAC 2015
Editors	Dongwan Shin
Publisher	Association for Computing Machinery
Pages	922-925
Number of pages	4
ISBN (Electronic)	9781450331968
DOIs	https://doi.org/10.1145/2695664.2696055
State	Published - 13 Apr 2015
Event	30th Annual ACM Symposium on Applied Computing, SAC 2015 - Salamanca, Spain Duration: 13 Apr 2015 → 17 Apr 2015

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing
Volume	13-17-April-2015

Conference

Conference	30th Annual ACM Symposium on Applied Computing, SAC 2015
Country/Territory	Spain
City	Salamanca
Period	13/04/15 → 17/04/15

Keywords

Concepts
Document clustering
Tensor space model
Text mining
Vector space model
Wikipedia

Access to Document

10.1145/2695664.2696055

Cite this

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title = "Semantically enriching text representation model for document clustering",

abstract = "This paper presents a novel text space model that represents textual documents for document clustering, which contains the 'concept' space independently of the 'document' and 'term' spaces. The text model described here represents documents as matrices (i.e., 2nd-order tensors), and a document corpus is represented as a 3rd-order tensor. For this, it is necessary to produce the concept vector for each term that occurs in a given document, which is related to word sense disambiguation. As an external knowledge source for concept weighting, we employ the Wikipedia encyclopedia.",

keywords = "Concepts, Document clustering, Tensor space model, Text mining, Vector space model, Wikipedia",

author = "Kim, {Han Joon} and Hong, {Kee Joo} and Chang, {Jae Young}",

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language = "English",

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Kim, HJ, Hong, KJ & Chang, JY 2015, Semantically enriching text representation model for document clustering. in D Shin (ed.), 2015 Symposium on Applied Computing, SAC 2015. Proceedings of the ACM Symposium on Applied Computing, vol. 13-17-April-2015, Association for Computing Machinery, pp. 922-925, 30th Annual ACM Symposium on Applied Computing, SAC 2015, Salamanca, Spain, 13/04/15. https://doi.org/10.1145/2695664.2696055

Semantically enriching text representation model for document clustering. / Kim, Han Joon; Hong, Kee Joo; Chang, Jae Young.
2015 Symposium on Applied Computing, SAC 2015. ed. / Dongwan Shin. Association for Computing Machinery, 2015. p. 922-925 (Proceedings of the ACM Symposium on Applied Computing; Vol. 13-17-April-2015).

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

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KW - Tensor space model

KW - Text mining

KW - Vector space model

KW - Wikipedia

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Semantically enriching text representation model for document clustering

Abstract

Publication series

Conference

Keywords

Access to Document

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