Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
A vector space model for automatic indexing
Communications of the ACM
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Combining Classifiers for word sense disambiguation
Natural Language Engineering
Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences
Computational Linguistics
Scaling distributional similarity to large corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the 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
Semantic inference at the lexical-syntactic level
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
One distributional memory, many semantic spaces
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Paraphrase assessment in structured vector space: exploring parameters and datasets
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Ranking paraphrases in context
TextInfer '09 Proceedings of the 2009 Workshop on Applied Textual Inference
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
Measuring distributional similarity in context
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Integrating logical representations with probabilistic information using Markov logic
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Exemplar-based word-space model for compositionality detection: shared task system description
DiSCo '11 Proceedings of the Workshop on Distributional Semantics and Compositionality
Towards a probabilistic model for lexical entailment
TIWTE '11 Proceedings of the TextInfer 2011 Workshop on Textual Entailment
Latent vector weighting for word meaning in context
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A quick tour of word sense disambiguation, induction and related approaches
SOFSEM'12 Proceedings of the 38th international conference on Current Trends in Theory and Practice of Computer Science
Evaluating distributional models of semantics for syntactically invariant inference
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
A comparison of models of word meaning in context
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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
A new benchmark dataset with production methodology for short text semantic similarity algorithms
ACM Transactions on Speech and Language Processing (TSLP)
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This paper describes ongoing work on distributional models for word meaning in context. We abandon the usual one-vector-per-word paradigm in favor of an exemplar model that activates only relevant occurrences. On a paraphrasing task, we find that a simple exemplar model outperforms more complex state-of-the-art models.