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Word sense induction using word embeddings and community detection in complex networks

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  • Corrêa, Edilson A.
  • Amancio, Diego R.

Abstract

Word Sense Induction (WSI) is the ability to automatically induce word senses from corpora. The WSI task was first proposed to overcome the limitations of manually annotated corpus that are required in word sense disambiguation systems. Even though several works have been proposed to induce word senses, existing systems are still very limited in the sense that they make use of structured, domain-specific knowledge sources. In this paper, we devise a method that leverages recent findings in word embeddings research to generate context embeddings, which are embeddings containing information about the semantical context of a word. In order to induce senses, we modeled the set of ambiguous words as a complex network. In the generated network, two instances (nodes) are connected if the respective context embeddings are similar. Upon using well-established community detection methods to cluster the obtained context embeddings, we found that the proposed method yields excellent performance for the WSI task. Our method outperformed competing algorithms and baselines, in a completely unsupervised manner and without the need of any additional structured knowledge source.

Suggested Citation

  • Corrêa, Edilson A. & Amancio, Diego R., 2019. "Word sense induction using word embeddings and community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 180-190.
  • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:180-190
    DOI: 10.1016/j.physa.2019.02.032
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    References listed on IDEAS

    as
    1. Diego R Amancio, 2015. "Probing the Topological Properties of Complex Networks Modeling Short Written Texts," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
    2. Camilo Akimushkin & Diego Raphael Amancio & Osvaldo Novais Oliveira Jr., 2017. "Text Authorship Identified Using the Dynamics of Word Co-Occurrence Networks," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-15, January.
    3. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "Hybrid self-optimized clustering model based on citation links and textual features to detect research topics," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    4. Diego Raphael Amancio, 2015. "Comparing the topological properties of real and artificially generated scientific manuscripts," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1763-1779, December.
    5. Silva, Filipi N. & Amancio, Diego R. & Bardosova, Maria & Costa, Luciano da F. & Oliveira, Osvaldo N., 2016. "Using network science and text analytics to produce surveys in a scientific topic," Journal of Informetrics, Elsevier, vol. 10(2), pages 487-502.
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    Cited by:

    1. Guerreiro, Lucas & Silva, Filipi N. & Amancio, Diego R., 2024. "Recovering network topology and dynamics from sequences: A machine learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    2. Davi Alves Oliveira & Hernane Borges de Barros Pereira, 2024. "Modeling texts with networks: comparing five approaches to sentence representation," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(6), pages 1-12, June.
    3. Corrêa, Edilson A. & Marinho, Vanessa Q. & Amancio, Diego R., 2020. "Semantic flow in language networks discriminates texts by genre and publication date," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).

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