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Application of Artificial Neural Networks in Morphological Tagging of Czech

Morphological tagging is an important problem in the area of computational linguistics as it underlies other crucial tasks such as syntactic parsing and machine translation. Nowadays, the problem is being most commonly solved by a statistical approach. Artificial neural networks (ANN) represent another promising approach to this kind of problems for which the exact algorithmic solution is unknown or not efficient enough. In this paper we present the results obtained by application of the well-known backpropagation (BP) neural network in several types of experiments. We have focused on the Czech morphology, because its morphological system is very rich and no experiments concerning application of artificial neural networks have been carried out for this language. First, we have verified on a set of preliminary experiments that the neural network is capable of capturing the Czech morphology, which, secondly, served also for determination of appropriate network and context parameters. Thirdly, we have used neural networks for a voting experiment. The aim of the voting experiment was to select the correct tag (if present) from the outputs of two statistical taggers, the Markov model tagger and the Featurebased tagger. In this experiment, BP showed higher tagging precision (93,56%) than any of the input statistical methods (92,74%, 92,58%) and exceeded even the currently best available statistical result (93,47%). BP has proved to be a worthy post-processing tool that is able to perform implicit evaluation on complementary aspects of different statistical approaches.