Measuring short text semantic similarity using multiple measurements

Tian Tian Zhu; Man Lan

doi:10.1109/ICMLC.2013.6890395

Measuring short text semantic similarity using multiple measurements

Tian Tian Zhu^*
, Man Lan

^*Corresponding author for this work

School of Computer Science and Technology

East China Normal University

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

2 Scopus citations

Abstract

In this paper, we present a Support Vector Regression (SVR) system to measure the semantic similarity of short texts by com-bining multiple similarity measurements, i.e., string similarity,knowledge-based similarity, corpus-based similarity, syntactic de-pendency similarity, number similarity and machine translation similarity. Experiments on the five data sets of SemEvai 2012 Semantic Text Similarity (STS) task show that our system performs best on two data sets, and second best on another two data sets .

Original language	English
Title of host publication	Proceedings - International Conference on Machine Learning and Cybernetics
Publisher	IEEE Computer Society
Pages	808-813
Number of pages	6
ISBN (Electronic)	9781479902576
DOIs	https://doi.org/10.1109/ICMLC.2013.6890395
State	Published - 2013
Event	12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China Duration: 14 Jul 2013 → 17 Jul 2013

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
Volume	2
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	12th International Conference on Machine Learning and Cybernetics, ICMLC 2013
Country/Territory	China
City	Tianjin
Period	14/07/13 → 17/07/13

Keywords

Semantic similarity
Short text
Support Vector Regression

Access to Document

10.1109/ICMLC.2013.6890395

Cite this

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title = "Measuring short text semantic similarity using multiple measurements",

abstract = "In this paper, we present a Support Vector Regression (SVR) system to measure the semantic similarity of short texts by com-bining multiple similarity measurements, i.e., string similarity,knowledge-based similarity, corpus-based similarity, syntactic de-pendency similarity, number similarity and machine translation similarity. Experiments on the five data sets of SemEvai 2012 Semantic Text Similarity (STS) task show that our system performs best on two data sets, and second best on another two data sets .",

keywords = "Semantic similarity, Short text, Support Vector Regression",

author = "Zhu, \{Tian Tian\} and Man Lan",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.; 12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 ; Conference date: 14-07-2013 Through 17-07-2013",

year = "2013",

doi = "10.1109/ICMLC.2013.6890395",

language = "英语",

series = "Proceedings - International Conference on Machine Learning and Cybernetics",

publisher = "IEEE Computer Society",

pages = "808--813",

booktitle = "Proceedings - International Conference on Machine Learning and Cybernetics",

address = "美国",

}

Zhu, TT & Lan, M 2013, Measuring short text semantic similarity using multiple measurements. in Proceedings - International Conference on Machine Learning and Cybernetics., 6890395, Proceedings - International Conference on Machine Learning and Cybernetics, vol. 2, IEEE Computer Society, pp. 808-813, 12th International Conference on Machine Learning and Cybernetics, ICMLC 2013, Tianjin, China, 14/07/13. https://doi.org/10.1109/ICMLC.2013.6890395

Measuring short text semantic similarity using multiple measurements. / Zhu, Tian Tian; Lan, Man.
Proceedings - International Conference on Machine Learning and Cybernetics. IEEE Computer Society, 2013. p. 808-813 6890395 (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 2).

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

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AB - In this paper, we present a Support Vector Regression (SVR) system to measure the semantic similarity of short texts by com-bining multiple similarity measurements, i.e., string similarity,knowledge-based similarity, corpus-based similarity, syntactic de-pendency similarity, number similarity and machine translation similarity. Experiments on the five data sets of SemEvai 2012 Semantic Text Similarity (STS) task show that our system performs best on two data sets, and second best on another two data sets .

KW - Semantic similarity

KW - Short text

KW - Support Vector Regression

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Measuring short text semantic similarity using multiple measurements

Abstract

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Keywords

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