Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Machine Learning
Tagging English text with a probabilistic model
Computational Linguistics
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Specifying a shallow grammatical representation for parsing purposes
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Probabilistic tagging with feature structures
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Part-of-speech tagging with neural networks
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
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An implementation of a Spanish POS tagger is described in this paper. This implementation combines three basic approaches: a single word tagger based on decision trees; a POS tagger based on a new learning model called the Multiattribute Prediction Suffix Graph; an d a feature structure set of tags. Using decision trees for single word tagging allows the tagger to work without a lexicon that enumerates possible tags only. Moreover, it decreases the error rate because there are no unknown words. The feature structure set of tags is advantageous when the available training corpus is small and the tag set large, which can be the case with morphologically rich languages such as Spanish. Finally, the multiattribute prediction suffix graph model training is more efficient than traditional full-order Markov models and achieves better accuracy.