Title

SEWordSim: Software-Specific Word Similarity Database

Publication Type

Conference Proceeding Article

Publication Date

6-2014

Abstract

Measuring the similarity of words is important in accurately representing and comparing documents, and thus improves the results of many natural language processing (NLP) tasks. The NLP community has proposed various measurements based on WordNet, a lexical database that contains relationships between many pairs of words. Recently, a number of techniques have been proposed to address software engineering issues such as code search and fault localization that require understanding natural language documents, and a measure of word similarity could improve their results. However, WordNet only contains information about words senses in general-purpose conversation, which often differ from word senses in a software-engineering context, and the software-specific word similarity resources that have been developed rely on data sources containing only a limited range of words and word uses. In recent work, we have proposed a word similarity resource based on information collected automatically from StackOverflow. We have found that the results of this resource are given scores on a 3-point Likert scale that are over 50% higher than the results of a resource based on WordNet. In this demo paper, we review our data collection methodology and propose a Java API to make the resulting word similarity resource useful in practice. The SEWordSim database and related information can be found at http://goo.gl/BVEAs8. Demo video is available at http://goo.gl/dyNwyb.

Keywords

word similiarity, java

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ICSE Companion 2014: 36th International Conference on Software Engineering: Proceedings: May 31-June 7, 2014, Hyderabad, India

First Page

568

Last Page

571

ISBN

9781450327688

Identifier

10.1145/2591062.2591071

Publisher

ACM

City or Country

New York

Additional URL

http://dx.doi.org/10.1145/2591062.2591071