Topic Recognition and Key-Phrase Extraction with Phrase Representation Learning
Zhang Jinzhu1,2(),Yu Wenqian1
1School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China 2Jiangsu Province Social Public Safety Science and Technology Collaborative Innovation Center, Nanjing 210094, China
[Objective] This paper designs a topic recognition and key-phrase extraction method based on phrase representation learning,aiming to address this issue from more specific perspective. [Methods] First, we constructed sequence for extracted phrases with dependency syntax analysis. Then, we modified the word representation learning model to process the phrase semantic vectors. Third, we developed topic recognition method based on the vector clustering technique. Fourth, we constructed the sequence of phrase topics with the phrases and the corresponding topic category numbers. Finally, we proposed a Topic-Phrase to Vector (TP2Vec) model to extract topic related phrases. [Results] Compared with the LDA model, the average similarity among topics of the proposed model was reduced by up-to 0.27. The extracted representative words were semantically related to the topics, and the results were more readable and interpretable. [Limitations] More research is needed to examine the proposed method with data sets from other fields. [Conclusions] The proposed method could effectively identify research topics and related phrases, which might be applied to other fields.
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