Unsupervised Keyword Extraction Methods Based on a Word Graph Network

Hongbin Wang, Jingzhen Ye, Zhengtao Yu, Jian Wang and Cunli Mao
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Hongbin Wang: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China
Jingzhen Ye: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China
Zhengtao Yu: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China
Jian Wang: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China
Cunli Mao: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, China

International Journal of Ambient Computing and Intelligence (IJACI), 2020, vol. 11, issue 2, 68-79

Abstract: Supervised keyword extraction methods usually require a large human-annotated corpus to train the model. Expensive manual labeling has made unsupervised technology using word graph networks attractive. Traditional word graph networks simply consider the co-occurrence relationship of words or the topological structure of the network, ignoring the influence of semantic relations between words on keyword extraction. To solve these problems, an unsupervised keyword extraction method based on word graph networks for both Chinese and English is proposed. This method uses word embedding to applying a “word attraction score” to semantic relevance between words in a document. Combination of the bias weight of the node and a weighted PageRank algorithm is used to compute the final scores of words. The experimental results demonstrate that the method is more effective than the traditional methods.

Date: 2020
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