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A co-occurrence based approach of automatic keyword expansion using mass diffusion

Author

Listed:
  • Xicheng Yin

    (Tongji University)

  • Hongwei Wang

    (Tongji University)

  • Pei Yin

    (University of Shanghai for Science and Technology)

  • Hengmin Zhu

    (Nanjing University of Posts and Telecommunications)

  • Zhenyu Zhang

    (Tongji University)

Abstract

The performance of keyword expansion in prior methods is often enhanced by adopting external knowledge. Given a set of initial keywords, this paper is motivated to propose a novel method to expand semantically or conceptually related keywords from domain corpus by employing mass diffusion. A bipartite word network is thus constructed based on co-occurrence relations between initial keywords and candidate words. The expanded keywords are identified via two-step mass diffusion which is carried out in the bipartite network. Experimental results prove that the proposed method outperforms both the typical statistical-based approach and graph-based approach. Our research is expected to complement the theoretical framework of keyword expansion and is applicable to the scenarios of query expansion, thesaurus construction, and text clustering.

Suggested Citation

  • Xicheng Yin & Hongwei Wang & Pei Yin & Hengmin Zhu & Zhenyu Zhang, 2020. "A co-occurrence based approach of automatic keyword expansion using mass diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1885-1905, September.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:3:d:10.1007_s11192-020-03601-7
    DOI: 10.1007/s11192-020-03601-7
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    References listed on IDEAS

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    1. M. Shamim Khan & Sebastian Khor, 2004. "Enhanced Web document retrieval using automatic query expansion," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(1), pages 29-40, January.
    2. Helen J. Peat & Peter Willett, 1991. "The limitations of term co‐occurrence data for query expansion in document retrieval systems," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 42(5), pages 378-383, June.
    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. LAMBIOTTE, Renaud & DELVENNE, Jean-Charles & BARAHONA, Mauricio, 2014. "Random walks, Markov processes and the multiscale modular organization of complex network," LIDAM Reprints CORE 2660, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Shuqing Li & Ying Sun & Dagobert Soergel, 2015. "A new method for automatically constructing domain-oriented term taxonomy based on weighted word co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 1023-1042, June.
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    Cited by:

    1. Ying Lian & Xiaofeng Lin & Xuefan Dong & Shengjie Hou, 2022. "A Normalized Rich-Club Connectivity-Based Strategy for Keyword Selection in Social Media Analysis," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    2. Jinqing Yang & Xiufeng Cheng & Guanghui Ye & Yuchen Zhang, 2024. "Understanding scientific knowledge evolution patterns based on egocentric network perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 6719-6750, November.

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