Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 8
2273-2278,2283

Topic clustering of academic literature abstracts based on WBLDA

Pan Xiaoying1,2
Wu Zhe1
Chen Liu1
Yang Fang1
1. School of Computer Science & Technology, Xi'an University of Posts & Telecommunications, Xi'an 710121, China
2. Shaanxi Key Laboratory of Network Data Analysis & Intelligent Processing, Xi'an 710121, China
DOI: 10.19734/j.issn.1001-3695.2019.02.0040

Abstract

In order to save time and read academic literature information efficiently, this paper proposed a topic clustering model for academic literature abstract the author-level topic clustering model(WBLDA). Firstly, in the pre-processing stage, the model defined a word segmentation dictionary and a stopword dictionary which accorded with the characteristics of academic literature abstract to solve the problem of inaccurate segmentation of academic literature abstract. In the feature extraction stage, it proposed an enhanced word frequency feature extraction method(ITF-IDF), which used word frequency amplification method to increase word frequency, weaken the influence of text length on the feature weight, and extracted more in line with academic literature abstract. Finally, aiming at the disadvantage of neglecting the author as an important attribute in the traditional topic model, this paper introduced the author information of academic literature summary into the topic clustering model, and constructed the WBLDA model of document-topic+author-word. At the same time, it used Bayesian criterion to optimize the number of topics in the topic clustering model. The simulation results of academic literature summary dataset show that the feature extraction accuracy of ITF-IDF is higher than the TF-IDF. Compared with LDA, the clustering purity and F-score value of WBLDA are higher, and the selected topics are more accurate and represent the academic direction of abstract.

Foundation Support

陕西省教育厅专项科研计划项目(17JK0687)
西安市科技创新引导项目(201805040YD18CG24(7))

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.02.0040
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 8
Section: Algorithm Research & Explore
Pages: 2273-2278,2283
Serial Number: 1001-3695(2020)08-006-2273-06

Publish History

[2020-08-05] Printed Article

Cite This Article

潘晓英, 伍哲, 陈柳, 等. 基于WBLDA的学术文献摘要主题聚类 [J]. 计算机应用研究, 2020, 37 (8): 2273-2278,2283. (Pan Xiaoying, Wu Zhe, Chen Liu, et al. Topic clustering of academic literature abstracts based on WBLDA [J]. Application Research of Computers, 2020, 37 (8): 2273-2278,2283. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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