Class-based probability estimation using a semantic hierarchy
Computational Linguistics
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Generalizing case frames using a thesaurus and the MDL principle
Computational Linguistics
Explaining away ambiguity: learning verb selectional preference with Bayesian networks
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Evaluating and combining approaches to selectional preference acquisition
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
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We present a cognitive model of inducing verb selectional preferences from individual verb usages. The selectional preferences for each verb argument are represented as a probability distribution over the set of semantic properties that the argument can possess---a semantic profile. The semantic profiles yield verb-specific conceptualizations of the arguments associated with a syntactic position. The proposed model can learn appropriate verb profiles from a small set of noisy training data, and can use them in simulating human plausibility judgments and analyzing implicit object alternation.