DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Principle-based parsing without overgeneration
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
More accurate tests for the statistical significance of result differences
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
The distributional inclusion hypotheses and lexical entailment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A structured vector space model for word meaning in context
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SemEval-2007 task 10: English lexical substitution task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Paraphrase assessment in structured vector space: exploring parameters and datasets
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Contextualizing semantic representations using syntactically enriched vector models
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Exemplar-based models for word meaning in context
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
What is word meaning, really?: (and how can distributional models help us describe it?)
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
Expectation vectors: a semiotics inspired approach to geometric lexical-semantic representation
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
Measuring distributional similarity in context
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Topic models for meaning similarity in context
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Latent vector weighting for word meaning in context
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
An inference-based model of word meaning in context as a paraphrase distribution
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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We present a vector space model that supports the computation of appropriate vector representations for words in context, and apply it to a paraphrase ranking task. An evaluation on the SemEval 2007 lexical substitution task data shows promising results: the model significantly outperforms a current state of the art model, and our treatment of context is effective.