Automatic WordNet Construction Using Markov Chain Monte Carlo



Document title: Automatic WordNet Construction Using Markov Chain Monte Carlo
Journal: Polibits
Database: PERIÓDICA
System number: 000373733
ISSN: 1870-9044
Authors: 1
1
1
1
Institutions: 1University of Tehran, College of Engineering, Teherán. Irán
Year:
Season: Jul-Dic
Number: 46
Pages: 13-22
Country: México
Language: Inglés
Document type: Artículo
Approach: Experimental, aplicado
English abstract 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
Disciplines: Ciencias de la computación
Keyword: Lingüística computacional,
Redes semánticas,
Ontología,
Inferencia bayesiana
Keyword: Computer science,
Computing linguistics,
Semantic networks,
Ontology,
Bayesian inference
Full text: Texto completo (Ver HTML)