Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Natural Language Information Processing: A Computer Grammmar of English and Its Applications
Natural Language Information Processing: A Computer Grammmar of English and Its Applications
A reestimation algorithm for probabilistic dependency grammars
Natural Language Engineering
Automatic learning for semantic collocation
ANLC '92 Proceedings of the third conference on Applied natural language processing
Learning parse and translation decisions from examples with rich context
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
Word clustering and disambiguation based on co-occurrence data
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Detecting errors in automatically-parsed dependency relations
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
| Hi-index | 0.00 |
NLP systems will be more portable among medical domains if acquisition of semantic lexicons can be facilitated. We are pursuing lexical acquisition through the syntactic relationships of words in medical corpora. Therefore we require a syntactic parser which is flexible, portable, captures head-modifier pairs and does not require a large training set. We have designed a dependency grammar parser that learns through a transformational-based algorithm. We propose a novel design for templates and transformations which capitalize on the dependency structure directly and produces human-readable rules. Our parser achieved a 77% accurate parse training on only 830 sentences. Further work will evaluate the usefulness of this parse for lexical acquisition.