Term Based Semantic Clusters for Very Short Text Classification.

Jasper Paalman; Shantanu Mullick; Kalliopi Zervanou; Yingqian Zhang

doi:10.26615/978-954-452-056-4_102

Term Based Semantic Clusters for Very Short Text Classification.

Jasper Paalman, Shantanu Mullick, Kalliopi Zervanou, Yingqian Zhang

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

7 Citations (Scopus)

Abstract

Very short texts, such as tweets and in-voices, present challenges in classification.Although term occurrences are strong in-dicators of content, in very short texts, thesparsity of these texts makes it difficult tocapture important semantic relationships.A solution calls for a method that not onlyconsiders term occurrence, but also han-dles sparseness well. In this work, we in-troduce such an approach, theTermBasedSemanticClusters (TBSeC) that employsterms to create distinctive semantic con-cept clusters. These clusters are rankedusing a semantic similarity function whichin turn defines a semantic feature spacethat can be used for text classification. Ourmethod is evaluated in an invoice classifi-cation task. Compared to well-known con-tent representation methods the proposedmethod performs competitively.

Original language	English
Title of host publication	Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Editors	Galia Angelova, Ruslan Mitkov, Ivelina Nikolova, Irina Temnikova
Publisher	Incoma Ltd
Pages	878-887
Number of pages	10
ISBN (Electronic)	9789544520557
DOIs	https://doi.org/10.26615/978-954-452-056-4_102 https://doi.org/10.26615/978-954-452-056-4_102
Publication status	Published - Sept 2019
Externally published	Yes
Event	International Conference on Recent Advances in Natural Language Processing - Varna, Bulgaria Duration: 2 Sept 2019 → 4 Sept 2019 Conference number: 12 http://ranlp.org/archive/ranlp2019/start.php

Conference

Conference	International Conference on Recent Advances in Natural Language Processing
Abbreviated title	RANLP 2019
Country/Territory	Bulgaria
City	Varna
Period	2/09/19 → 4/09/19
Internet address	http://ranlp.org/archive/ranlp2019/start.php

Bibliographical note

DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

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Paalman, J., Mullick, S., Zervanou, K., & Zhang, Y. (2019). Term Based Semantic Clusters for Very Short Text Classification. In G. Angelova, R. Mitkov, I. Nikolova, & I. Temnikova (Eds.), Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (pp. 878-887). Incoma Ltd. https://doi.org/10.26615/978-954-452-056-4_102, https://doi.org/10.26615/978-954-452-056-4_102

Term Based Semantic Clusters for Very Short Text Classification. / Paalman, Jasper; Mullick, Shantanu; Zervanou, Kalliopi et al.
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019). ed. / Galia Angelova; Ruslan Mitkov; Ivelina Nikolova; Irina Temnikova. Incoma Ltd, 2019. p. 878-887.

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

Paalman, J, Mullick, S, Zervanou, K & Zhang, Y 2019, Term Based Semantic Clusters for Very Short Text Classification. in G Angelova, R Mitkov, I Nikolova & I Temnikova (eds), Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019). Incoma Ltd, pp. 878-887, International Conference on Recent Advances in Natural Language Processing , Varna, Bulgaria, 2/09/19. https://doi.org/10.26615/978-954-452-056-4_102, https://doi.org/10.26615/978-954-452-056-4_102

Paalman J, Mullick S, Zervanou K, Zhang Y. Term Based Semantic Clusters for Very Short Text Classification. In Angelova G, Mitkov R, Nikolova I, Temnikova I, editors, Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019). Incoma Ltd. 2019. p. 878-887 doi: 10.26615/978-954-452-056-4_102, 10.26615/978-954-452-056-4_102

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title = "Term Based Semantic Clusters for Very Short Text Classification.",

abstract = "Very short texts, such as tweets and in-voices, present challenges in classification.Although term occurrences are strong in-dicators of content, in very short texts, thesparsity of these texts makes it difficult tocapture important semantic relationships.A solution calls for a method that not onlyconsiders term occurrence, but also han-dles sparseness well. In this work, we in-troduce such an approach, theTermBasedSemanticClusters (TBSeC) that employsterms to create distinctive semantic con-cept clusters. These clusters are rankedusing a semantic similarity function whichin turn defines a semantic feature spacethat can be used for text classification. Ourmethod is evaluated in an invoice classifi-cation task. Compared to well-known con-tent representation methods the proposedmethod performs competitively.",

author = "Jasper Paalman and Shantanu Mullick and Kalliopi Zervanou and Yingqian Zhang",

note = "DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; International Conference on Recent Advances in Natural Language Processing , RANLP 2019 ; Conference date: 02-09-2019 Through 04-09-2019",

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AB - Very short texts, such as tweets and in-voices, present challenges in classification.Although term occurrences are strong in-dicators of content, in very short texts, thesparsity of these texts makes it difficult tocapture important semantic relationships.A solution calls for a method that not onlyconsiders term occurrence, but also han-dles sparseness well. In this work, we in-troduce such an approach, theTermBasedSemanticClusters (TBSeC) that employsterms to create distinctive semantic con-cept clusters. These clusters are rankedusing a semantic similarity function whichin turn defines a semantic feature spacethat can be used for text classification. Ourmethod is evaluated in an invoice classifi-cation task. Compared to well-known con-tent representation methods the proposedmethod performs competitively.

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Term Based Semantic Clusters for Very Short Text Classification.

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