Building a lexicon for machine translation: use of corpora for aspectual classification of verbs
Building a lexicon for machine translation: use of corpora for aspectual classification of verbs
Class-based n-gram models of natural language
Computational Linguistics
Automatic acquisition of word meaning from context
Automatic acquisition of word meaning from context
Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Morphological cues for lexical semantics
Morphological cues for lexical semantics
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Tagging English text with a probabilistic model
Computational Linguistics
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
HLT '93 Proceedings of the workshop on Human Language Technology
Deriving verbal and compositional lexical aspect for NLP applications
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
A categorial variation database for English
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Learning the countability of English nouns from corpus data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Automatic acquisition of semantic relationships from morphological relatedness
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Unsupervised word categorization using self-organizing maps and automatically extracted morphs
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
| Hi-index | 0.00 |
Most natural language processing tasks require lexical semantic information. Automated acquisition of this information would thus increase the robustness and portability of NLP systems. This paper describes an acquisition method which makes use of fixed correspondences between derivational affixes and lexical semantic information. One advantage of this method, and of other methods that rely only on surface characteristics of language, is that the necessary input is currently available.