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Study on Topic Extraction of Literatures Based on Weighted Semantic and Citation Relation
Received date: 2016-01-04
Revised date: 2016-04-20
Online published: 2016-05-05
[Purpose/significance] The traditional methods of topic extraction mainly extract the themes of literatures by keywords, abstracts and full texts, but their results are not comprehensive or have noises. The method which starts from the semantic of literature content and is combined with the citation content, can extract the themes of literatures more accurately. [Result/conclusion] This article proposes a literature topic extracting algorithm based on weighted semantic and citation relation for multi-documents. It builds the Labeled-LDA topic Model with the citation content and keywords of literatures, gets documents-topics probability distribution. Then it clusters documents through the K-means clustering method and extracts the topics of each type of documents. [Result/conclusion] In the experimental part, the test data are downloaded from the PubMed database. The result shows that the method can accurately extract the theme of literatures.
Key words： Labeled-LDA Model; citation content; topic extraction
Yang Chunyan , Pan Youneng , Zhao Li . Study on Topic Extraction of Literatures Based on Weighted Semantic and Citation Relation[J]. Library and Information Service, 2016 , 60(9) : 131 -138,146 . DOI: 10.13266/j.issn.0252-3116.2016.09.018
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