| Revista: | Polibits |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000373733 |
| ISSN: | 1870-9044 |
| Autores: | Fadaee, Marzieh1 Ghader, Hamidreza1 Faili, Heshaam1 Shakery, Azadeh1 |
| Instituciones: | 1University of Tehran, College of Engineering, Teherán. Irán |
| Año: | 2012 |
| Periodo: | Jul-Dic |
| Número: | 46 |
| Paginación: | 13-22 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | WordNet is used extensively as a major lexical resource in information retrieval tasks. However, the qualities of existing Persian WordNets are far from perfect. They are either constructed manually which limits the coverage of Persian words, or automatically which results in unsatisfactory precision. This paper presents a fully-automated approach for constructing a Persian WordNet: A Bayesian Model with Markov chain Monte Carlo (MCMC) estimation. We model the problem of constructing a Persian WordNet by estimating the probability of assigning senses (synsets) to Persian words. By applying MCMC techniques in estimating these probabilities, we integrate prior knowledge in the estimation and use the expected value of generated samples to give the final estimates. This ensures great performance improvement comparing with Maximum-Likelihood and Expectation-Maximization methods. Our acquired WordNet has a precision of 90.46% which is a considerable improvement in comparison with automatically-built WordNets in Persian |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Lingüística computacional, Redes semánticas, Ontología, Inferencia bayesiana |
| Keyword: | Computer science, Computing linguistics, Semantic networks, Ontology, Bayesian inference |
| Texto completo: | Texto completo (Ver HTML) |
