Abstract
Information Technologies have created many documents, which are the basis of information systems, capable of speeding up the process for which they were developed. These processes provide results trying to imitate human knowledge, but it is still essential to generate techniques that support each other to provide even more precise results. It is essential to incorporate techniques in the automatic extraction of topics to reflect results with greater coherence according to the topic in question. In this work, the behavior of the Latent Dirichlet Analysis (LDA) algorithm is studied by incorporating the hypernymy-type semantic relationship extracted from WordNet in order to improve the results obtained when applying LDA on a set of documents without the use of an external source of knowledge. The experimental results showed an improvement when incorporating hypernyms providing a 1.23 topic coherence for GoogleNews corpus.
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Acknowledgements
The authors would like to thank Universidad Autónoma Metropolitana, Azcapotzalco. The present work has been funded by the research project SI001-20 at UAM Azcapotzalco, partly supported by project VIEP 2021 at BUAP and by the Consejo Nacional de Ciencia y TecnologíÃÃ’¯Â¿Â½a (CONACYT) with the scholarship number 788155.
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Sánchez, A.L.L., Vidal, M.T., Reyes-Ortiz, J.A. (2021). Hypernyms-Based Topic Discovery Using LDA. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science(), vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_6
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