IEICE TRANSACTIONS on Information
Automatic Stop Word Generation for Mining Software Artifact Using Topic Model with Pointwise Mutual Information
Summary :
Mining software artifacts is a useful way to understand the source code of software projects. Topic modeling in particular has been widely used to discover meaningful information from software artifacts. However, software artifacts are unstructured and contain a mix of textual types within the natural text. These software artifact characteristics worsen the performance of topic modeling. Among several natural language pre-processing tasks, removing stop words to reduce meaningless and uninteresting terms is an efficient way to improve the quality of topic models. Although many approaches are used to generate effective stop words, the lists are outdated or too general to apply to mining software artifacts. In addition, the performance of the topic model is sensitive to the datasets used in the training for each approach. To resolve these problems, we propose an automatic stop word generation approach for topic models of software artifacts. By measuring topic coherence among words in the topic using Pointwise Mutual Information (PMI), we added words with a low PMI score to our stop words list for every topic modeling loop. Through our experiment, we proved that our stop words list results in a higher performance of the topic model than lists from other approaches.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.9 pp.1761-1772
- Publication Date
- 2019/09/01
- Publicized
- 2019/05/27
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7390
- Type of Manuscript
- PAPER
- Category
- Software Engineering
Authors
Jung-Been LEE
Korea University
Taek LEE
Sungshin University
Hoh Peter IN
Korea University
Keyword
Latest Issue
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Jung-Been LEE, Taek LEE, Hoh Peter IN, "Automatic Stop Word Generation for Mining Software Artifact Using Topic Model with Pointwise Mutual Information" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 9, pp. 1761-1772, September 2019, doi: 10.1587/transinf.2018EDP7390.
Abstract: Mining software artifacts is a useful way to understand the source code of software projects. Topic modeling in particular has been widely used to discover meaningful information from software artifacts. However, software artifacts are unstructured and contain a mix of textual types within the natural text. These software artifact characteristics worsen the performance of topic modeling. Among several natural language pre-processing tasks, removing stop words to reduce meaningless and uninteresting terms is an efficient way to improve the quality of topic models. Although many approaches are used to generate effective stop words, the lists are outdated or too general to apply to mining software artifacts. In addition, the performance of the topic model is sensitive to the datasets used in the training for each approach. To resolve these problems, we propose an automatic stop word generation approach for topic models of software artifacts. By measuring topic coherence among words in the topic using Pointwise Mutual Information (PMI), we added words with a low PMI score to our stop words list for every topic modeling loop. Through our experiment, we proved that our stop words list results in a higher performance of the topic model than lists from other approaches.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7390/_p
Copy
@ARTICLE{e102-d_9_1761,
author={Jung-Been LEE, Taek LEE, Hoh Peter IN, },
journal={IEICE TRANSACTIONS on Information},
title={Automatic Stop Word Generation for Mining Software Artifact Using Topic Model with Pointwise Mutual Information},
year={2019},
volume={E102-D},
number={9},
pages={1761-1772},
abstract={Mining software artifacts is a useful way to understand the source code of software projects. Topic modeling in particular has been widely used to discover meaningful information from software artifacts. However, software artifacts are unstructured and contain a mix of textual types within the natural text. These software artifact characteristics worsen the performance of topic modeling. Among several natural language pre-processing tasks, removing stop words to reduce meaningless and uninteresting terms is an efficient way to improve the quality of topic models. Although many approaches are used to generate effective stop words, the lists are outdated or too general to apply to mining software artifacts. In addition, the performance of the topic model is sensitive to the datasets used in the training for each approach. To resolve these problems, we propose an automatic stop word generation approach for topic models of software artifacts. By measuring topic coherence among words in the topic using Pointwise Mutual Information (PMI), we added words with a low PMI score to our stop words list for every topic modeling loop. Through our experiment, we proved that our stop words list results in a higher performance of the topic model than lists from other approaches.},
keywords={},
doi={10.1587/transinf.2018EDP7390},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Automatic Stop Word Generation for Mining Software Artifact Using Topic Model with Pointwise Mutual Information
T2 - IEICE TRANSACTIONS on Information
SP - 1761
EP - 1772
AU - Jung-Been LEE
AU - Taek LEE
AU - Hoh Peter IN
PY - 2019
DO - 10.1587/transinf.2018EDP7390
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2019
AB - Mining software artifacts is a useful way to understand the source code of software projects. Topic modeling in particular has been widely used to discover meaningful information from software artifacts. However, software artifacts are unstructured and contain a mix of textual types within the natural text. These software artifact characteristics worsen the performance of topic modeling. Among several natural language pre-processing tasks, removing stop words to reduce meaningless and uninteresting terms is an efficient way to improve the quality of topic models. Although many approaches are used to generate effective stop words, the lists are outdated or too general to apply to mining software artifacts. In addition, the performance of the topic model is sensitive to the datasets used in the training for each approach. To resolve these problems, we propose an automatic stop word generation approach for topic models of software artifacts. By measuring topic coherence among words in the topic using Pointwise Mutual Information (PMI), we added words with a low PMI score to our stop words list for every topic modeling loop. Through our experiment, we proved that our stop words list results in a higher performance of the topic model than lists from other approaches.
ER -
English
