Corpus processing for lexical acquisition
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
Tagging with Small Training Corpora
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
From grammar to lexicon: unsupervised learning of lexical syntax
Computational Linguistics - Special issue on using large corpora: II
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
On learning more appropriate Selectional Restrictions
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Automatic retrieval and clustering of similar words
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
Generalizing automatically generated selectional patterns
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Ontology learning from text: A look back and into the future
ACM Computing Surveys (CSUR)
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Partially parsed corpora is used for automatically extracting semantic and syntactic subcategorization information for words, helping to cluster them according to their sense which is highly restricted by the syntactic contexts where words do occur. In this paper we propose the use of a parsing platform, based on chart parsing and tabling, in order to check if the syntactic and semantic information extracted automatically leads to better parses than assuming that words do not subcategorize anything.