| Revue: | Computación y sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000411056 |
| ISSN: | 1405-5546 |
| Autores: | Hercig, Tomas1 Brychcín, Tomas1 Svoboda, Lukas1 Konkol, Michal1 Steinberger, Josef1 |
| Instituciones: | 1University of West Bohemia, Faculty of Applied Sciences, Plzen. República Checa |
| Año: | 2016 |
| Periodo: | Jul-Sep |
| Volumen: | 20 |
| Número: | 3 |
| Paginación: | 365-375 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | We examine the effectiveness of several unsupervised methods for latent semantics discovery as features for aspect-based sentiment analysis (ABSA). We use the shared task definition from SemEval 2014. In our experiments we use labeled and unlabeled corpora within the restaurants domain for two languages: Czech and English. We show that our models improve the ABSA performance and prove that our approach is worth exploring. Moreover, we achieve new state-of-the-art results for Czech. Another important contribution of our work is that we created two new Czech corpora within the restaurant domain for the ABSA task: one labeled for supervised training, and the other (considerably larger) unlabeled for unsupervised training. The corpora are available to the research community |
| Disciplinas: | Ciencias de la computación, Literatura y lingüística |
| Palabras clave: | Procesamiento de datos, Lingüística aplicada, Lingüística computacional, Análisis de textos, Semántica latente, Análisis de sentimiento |
| Keyword: | Computer science, Literature and linguistics, Data processing, Applied linguistics, Computing linguistics, Text analysis, Latent semantics, Sentiment analysis |
| Texte intégral: | Texto completo (Ver HTML) |
