Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text
Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Tagging accurately: don't guess if you know
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Tagging and morphological disambiguation of Turkish text
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Morphological disambiguation by voting constraints
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
CLAWS4: the tagging of the British National Corpus
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
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In this paper we present the results of the combination of stochastic and rule-based disambiguation methods applied to Basque language1. The methods we have used in disambiguation are Constraint Grammar formalism and an HMM based tagger developed within the MULTEXT project. As Basque is an agglutinative language, a morphological analyser is needed to attach all possible readings to each word. Then, CG rules are applied using all the morphological features and this process decreases morphological ambiguity of texts. Finally, we use the MULTEXT project tools to select just one from the possible remaining tags.Using only the stochastic method the error rate is about 14%, but the accuracy may be increased by about 2% enriching the lexicon with the unknown words. When both methods are combined, the error rate of the whole process is 3.5%. Considering that the training corpus is quite small, that the HMM model is a first order one and that Constraint Grammar of Basque language is still in progress, we think that this combined method can achieve good results, and it would be appropriate for other agglutinative languages.