基于主题N元语法模型的科技报告主题分析

doi:10.13998/j.cnki.issn1002-1248.2019.06.19-0550

Abstract

Abstract: As one of the important carriers of scientific & technical (S&T) intelligence, S&T reports can reflect the line of S&T development, recover the latest news of S&T fronts, and even insight the trends of S&T development. Researches on developing and utilizing S&T reports in our country mainly focus on the following: publication and distribution of S&T reports in the form of book and electrical publication; database construction; service mode; intelligent property and so on. The deep data mining on S&T reports remains largely under-studied. This work tries to discover the domain latent topics of S&T reports with the topical n-grams model. In order to determine the number of topics of S&T reports for some specific domain, the calculation method of perplexity of the topic n-grams model is put forward with the dynamic programming in this study. Finally, 70 domain topics are discovered from 1 344 S&T reports in the tumor domain, such as "molecular mechanisms/tumor cells", "system biology/key methods" and so on. Experimental results show that it is feasible and efficient to discover the latent topics from S&T reports with the topical n-grams model.

Key words: scientific and technical reports, topical n-grams model, topical analysis, perplexity, heat map

CLC Number:

G322

AN Xin, XU Shuo. Topical Analysis of Scientific and Technical Reports based on Topical N-Grams Model[J].Agricultural Library and Information, 2019, 31(6): 21-30.

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Topical Analysis of Scientific and Technical Reports based on Topical N-Grams Model

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