Automatic stochastic tagging of natural language texts
Computational Linguistics
Domain-specific knowledge acquisition for conceptual sentence analysis
Domain-specific knowledge acquisition for conceptual sentence analysis
Machine Learning
Empirical Learning of Natural Language Processing Task
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Coping with ambiguity and unknown words through probabilistic models
Computational Linguistics - Special issue on using large corpora: II
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second 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 syntax-based part-of-speech analyser
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Acquiring disambiguation rules from text
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Part-of-speech tagging with neural networks
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Learning part-of-speech guessing rules from lexicon: extension to non-concatenative operations
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Guessing parts-of-speech of unknown words using global information
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A collaborative framework for collecting Thai unknown words from the web
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
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This paper presents a decision-tree approach to the problems of part-of-speech disambiguation and unknown word guessing as they appear in Modern Greek, a highly inflectional language. The learning procedure is tag-set independent and reflects the linguistic reasoning on the specific problems. The decision trees induced are combined with a high-coverage lexicon to form a tagger that achieves 93, 5% overall disambiguation accuracy.