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Document keyword extraction based on semantic hierarchical graph model

Author

Listed:
  • Tingting Zhang

    (Nanjing Audit University)

  • Baozhen Lee

    (Nanjing Audit University)

  • Qinghua Zhu

    (Nanjing University)

  • Xi Han

    (Guangdong University of Finance and Economics)

  • Ke Chen

    (Nanjing Audit University)

Abstract

Keyword provide a brief profile of document contents and serve as an important method for quickly obtaining the document’s themes. Traditional keyword extraction methods are mostly based on statistical relationships between words, with no deeper understanding of the words’ structures. In addition, most studies to date performing keyword extraction are based on ranking-related measure values, without considering the cohesion of the extracted keyword set. In this paper, a keyword extraction method based on a semantic hierarchical graph model is proposed. First, the semantic graph for the document is constructed based on the hierarchical extraction of feature terms. Then, the keyword collection of the document is chosen from the constructed semantic graph. The keyword extraction method in this paper fully accounts for both the context of the keywords and the internal structure by which they are related. By mining the deep hidden structure of feature terms, the proposed method can effectively reveal the hierarchical association between terms within the semantic graph and obtain a keyword collection result with high probability. Moreover, several experiments conducted on released datasets show that our method outperforms the existing methods in terms of precision, recall, and F-measure.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:5:d:10.1007_s11192-023-04677-7
    DOI: 10.1007/s11192-023-04677-7
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    References listed on IDEAS

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    1. 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.
    2. Zara Nasar & Syed Waqar Jaffry & Muhammad Kamran Malik, 2018. "Information extraction from scientific articles: a survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1931-1990, December.
    3. Liu Yang & Keping Li & Hangfei Huang, 2018. "A new network model for extracting text keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 339-361, July.
    4. Garg, Muskan & Kumar, Mukesh, 2018. "The structure of word co-occurrence network for microblogs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 698-720.
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