Conceptual graphs for the analysis and generation of sentences
IBM Journal of Research and Development
Syntactic graphs: a representation for the union of all ambiguous parse trees
Computational Linguistics
Word association norms, mutual information, and lexicography
Computational Linguistics
Using Word Association for Syntactic Disambiguation
AI*IA Proceedings of the 2nd Congress of the Italian Association for Artificial Intelligence on Trends in Artificial Intelligence
Redefining the "Level" of the "Word"
Proceedings of the First SIGLEX Workshop on Lexical Semantics and Knowledge Representation
A generative grammar approach for the morphologic and morphosyntactic analysis of Italian
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
A structured representation of word-senses for semantic analysis
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
Subject-dependent co-occurrence and word sense disambiguation
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Automatic acquisition of subcategorization frames from untagged text
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Structural ambiguity and lexical relations
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
From N-grams to collocations: an evaluation of Xtract
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Automatic acquisition of the lexical semantics of verbs from sentence frames
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Automatically extracting and representing collocations for language generation
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Noun classification from predicate-argument structures
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Acquisition of lexical information: from a large textual Italian corpus
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Tagging for learning: collecting thematic relations from corpus
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 1
Finding a domain-appropriate sense inventory for semantically tagging a corpus
Natural Language Engineering
Might a semantic lexicon support hypertextual authoring?
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Automatic selection of class labels from a thesaurus for an effective semantic tagging of 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 alignment in parallel corpora
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
An experiment on learning appropriate Selectional Restrictions from a parsed corpus
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
A "not-so-shallow" parser for collocational analysis
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Clustering Syntactic Positions with Similar Semantic Requirements
Computational Linguistics
A non-negative tensor factorization model for selectional preference induction
Natural Language Engineering
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When implementing computational lexicons it is important to keep in mind the texts that a NLP system must deal with. Words relate to each other in many different, often queer, ways: this information is rarely found in dictionaries, and it is quite hard to be invented a priori, despite the imagination that linguists exhibit at inventing esoteric examples.In this paper we present the results of an experiment in learning from corpora the frequent selectional restrictions holding between content words. The method is based on the analysis of word associations augmented with syntactic markers and semantic tags. Word pairs are extracted by a morphosyntactic analyzer and clustered according to their semantic tags. A statistical measure is applied to the data to evaluate the significance of a detected relation. Clustered association data render the study of word associations more interesting with several respects: data are more reliable even for smaller corpora, more easy to interpret, and have many practical applications in NLP.