Automatic Part-Of-Speech tagging of Thai corpus using neural networks

Abstract

A cost-effective method for Part-Of-Speech (POS) tagging of a Thai corpus using neural networks is proposed. Computer experiments show that this method has a success rate of over 80% for tagging text of untrained data, and an error rate below 8%. These results are much better than those obtained by conventional table lookup methods. Some experiments comparing original and various modified back-propagation algorithms for training the neural network tagger are also conducted. Results of these experiments show that the learning algorithm with DBDB adaptation rule at a semi-batch update mode is the best one for tagging text in terms of convergence rate and computaional complexity.