| Journal: | Computación y sistemas |
| Database: | |
| System number: | 000560647 |
| ISSN: | 1405-5546 |
| Authors: | Lezama Sánchez, Ana Laura1 Tovar Vidal, Mireya1 Reyes Ortiz, José Alejandro2 |
| Institutions: | 1Benemérita Universidad Autónoma de Puebla, Facultad de Ciencias de la Computación, México 2Universidad Autónoma Metropolitana, Departamento de Sistemas, México |
| Year: | 2022 |
| Season: | Ene-Mar |
| Volumen: | 26 |
| Number: | 1 |
| Pages: | 149-160 |
| Country: | México |
| Language: | Inglés |
| English abstract | Information Technologies have generated large amounts of documents available for analysis and use. Information systems can provide the user with the necessary data for a specific purpose without human intervention, saving time in providing the response expected by the user. Some traditional models of topic discovery provide essential information in the literature, but it is still necessary to incorporate the knowledge that a person can use when reading a document. In this work, an analysis of the behavior of the techniques of Latent Dirichlet Analysis, Latent Semantic Analysis, and Probabilistic Latent Semantic Analysis is carried out incorporating the semantic relationships of the type hypernym, hyponym, synonymy, holonymy, and meronymy extracted from an external source of knowledge as WordNet. In order to improve the results obtained by applying the three mentioned techniques in a set of documents without adding external knowledge. Compared to the initial results, our experimental results improved when incorporating semantic relationships, such as hypernyms and synonyms. The best result was obtained when using the Lesk algorithm for word sense disambiguation and subsequently applying Latent Dirichlet Analysis. |
| Keyword: | Topic discovery, Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), WordNet, Latent Dirichlet Analysis (LDA) |
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