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Using word embedding to detect keywords in texts modeled as complex networks

Author

Listed:
  • Jorge A. V. Tohalino

    (University of São Paulo)

  • Thiago C. Silva

    (Universidade Católica de Brasília)

  • Diego R. Amancio

    (University of São Paulo)

Abstract

Detecting keywords in texts is a task of paramount importance for many text mining applications. Graph-based techniques have been commonly used to automatically find the key concepts in texts. However, the integration of valuable information provided by embeddings to enrich the graph structure has not been widely used. In this context, this paper aims to address the following question: can the quality of extracted keywords from a co-occurrence network be enhanced by integrating embeddings to enrich the network structure? In the adopted model, texts are represented as co-occurrence networks, where nodes are words and edges are established either by contextual or semantical similarity. Two embedding approaches were used: Word2vec and Bidirectional Encoder Representations from Transformers (BERT). The results indicate that using virtual edges can effectively enhance the discriminative capacity of co-occurrence networks. The best performance was achieved by incorporating a limited proportion of virtual (embedding) edges. A comparison of the structural and dynamical network metrics demonstrated that the degree, PageRank, and accessibility metrics exhibited superior performance in the proposed model.

Suggested Citation

  • Jorge A. V. Tohalino & Thiago C. Silva & Diego R. Amancio, 2024. "Using word embedding to detect keywords in texts modeled as complex networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3599-3623, July.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:7:d:10.1007_s11192-024-05055-7
    DOI: 10.1007/s11192-024-05055-7
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    References listed on IDEAS

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    1. Beakcheol Jang & Inhwan Kim & Jong Wook Kim, 2019. "Word2vec convolutional neural networks for classification of news articles and tweets," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-20, August.
    2. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    3. J. P. Herrera & P. A. Pury, 2008. "Statistical keyword detection in literary corpora," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(1), pages 135-146, May.
    4. Jeaneth Machicao & Edilson A Corrêa Jr. & Gisele H B Miranda & Diego R Amancio & Odemir M Bruno, 2018. "Authorship attribution based on Life-Like Network Automata," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-21, March.
    5. Adilson Vital & Diego R. Amancio, 2022. "A comparative analysis of local similarity metrics and machine learning approaches: application to link prediction in author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6011-6028, October.
    6. Carretero-Campos, C. & Bernaola-Galván, P. & Coronado, A.V. & Carpena, P., 2013. "Improving statistical keyword detection in short texts: Entropic and clustering approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1481-1492.
    7. Diego R. Amancio & Osvaldo N. Oliveira jr & Luciano F. Costa, 2015. "Topological-collaborative approach for disambiguating authors’ names in collaborative networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 465-485, January.
    8. Tohalino, Jorge V. & Amancio, Diego R., 2018. "Extractive multi-document summarization using multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 526-539.
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