A N-gram based approach to auto-extracting topics from research articles
Document Type
Article
Publication Date
1-1-2022
Abstract
A lot of manual work goes into identifying a topic for an article. With a large volume of articles, the manual process can be exhausting. Our approach aims to address this issue by automatically extracting topics from the text of large numbers of articles. This approach takes into account the efficiency of the process. Based on existing N-gram analysis, our research examines how often certain words appear in documents in order to support automatic topic extraction. In order to improve efficiency, we apply custom filtering standards to our research. Additionally, delete as many noncritical or irrelevant phrases as possible. In this way, we can ensure we are selecting unique keyphrases for each article, which capture its core idea1. For our research, we chose to center on the autonomous vehicle domain, since the research is relevant to our daily lives. We have to convert the PDF versions of most of the research papers into editable types of files such as TXT. This is because most of the research papers are only in PDF format. To test our proposed idea of automating, numerous articles on robotics have been selected. Next, we evaluate our approach by comparing the result with other models.
Keywords
Automatic topic extraction, Frequency statistic, Keyphrase, N-gram
Funders
FDCT-NSFC [0066/2019/AFJ],Research and Application of Cooperative MultiAgent Platform for Zhuhai-Macao Manufacturing Service(MOST-FDCT) [0058/2019/AMJ],National Key Research and Development Program of China [2020YFB806504]
Publication Title
Journal of Intelligent & Fuzzy Systems
Volume
43
Issue
5
Publisher
IOS Press
Publisher Location
NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS