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Unsupervised Keyword Extraction Methods Based on a Word Graph Network

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hongbin Wang & Jingzhen Ye & Zhengtao Yu & Jian Wang & Cunli Mao, 2020. "Unsupervised Keyword Extraction Methods Based on a Word Graph Network," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 11(2), pages 68-79, April.
    • Handle: RePEc:igg:jaci00:v:11:y:2020:i:2:p:68-79
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    Citations

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    Cited by:

    1. Tingting Zhang & Baozhen Lee & Qinghua Zhu & Xi Han & Ke Chen, 2023. "Document keyword extraction based on semantic hierarchical graph model," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2623-2647, May.
    2. Esra Gündoğan & Mehmet Kaya, 2022. "A novel hybrid paper recommendation system using deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3837-3855, July.

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