Class-based probability estimation using a semantic hierarchy
Computational Linguistics
Explaining away ambiguity: learning verb selectional preference with Bayesian networks
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Computational Linguistics
Linguistically motivated large-scale NLP with C&C and boxer
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Natural Language Processing with Python
Natural Language Processing with Python
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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We consider sentences of the form No X is too Y to Z, in which X is a noun phrase, Y is an adjective phrase, and Z is a verb phrase. Such constructions are ambiguous, with two possible (and opposite!) interpretations, roughly meaning either that "Every X Zs", or that "No X Zs". The interpretations have been noted to depend on semantic and pragmatic factors. We show here that automatic disambiguation of this pragmatically complex construction can be largely achieved by using features of the lexical semantic properties of the verb (i.e., Z) participating in the construction. We discuss our experimental findings in the context of construction grammar, which suggests a possible account of this phenomenon.