Measuring short Text Semantic Similarity using multiple measurements

Author

Tian-Tian Zhu ; Man Lan

Author_Institution

Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China

Volume

02

fYear

2013

fDate

14-17 July 2013

Firstpage

808

Lastpage

813

Abstract

In this paper, we present a Support Vector Regression (SVR) system to measure the semantic similarity of short texts by combining multiple similarity measurements, i.e., string similarity, knowledge-based similarity, corpus-based similarity, syntactic dependency similarity, number similarity and machine translation similarity. Experiments on the five data sets of SemEval 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

regression analysis; support vector machines; text analysis; STS; SVR system; SemEval 2012 semantic text similarity; corpus-based similarity; knowledge-based similarity; machine translation similarity; multiple measurements; multiple similarity measurements; number similarity; short text semantic similarity; string similarity; support vector regression; syntactic dependency similarity; Abstracts; Local area networks; NIST; Syntactics; Weight measurement; Semantic similarity; Short text; Support Vector Regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2013 International Conference on

Conference_Location

Tianjin

Type

conf

DOI

10.1109/ICMLC.2013.6890395

Filename

6890395