A vector space model for automatic indexing
Communications of the ACM
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Placing search in context: the concept revisited
ACM Transactions on Information Systems (TOIS)
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
The Journal of Machine Learning Research
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Discovery of inference rules for question-answering
Natural Language Engineering
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
More accurate tests for the statistical significance of result differences
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Relation between PLSA and NMF and implications
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Computational Statistics & Data Analysis
Fast collapsed gibbs sampling for latent dirichlet allocation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Language models based on semantic composition
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Context-theoretic semantics for natural language: an overview
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
Multi-prototype vector-space models of word meaning
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
Measuring the impact of sense similarity on word sense induction
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Latent vector weighting for word meaning in context
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Probabilistic models of similarity in syntactic context
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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
SemEval-2012 task 4: evaluating Chinese word similarity
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Improving word representations via global context and multiple word prototypes
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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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The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and paraphrases to word sense disambiguation and textual entailment. Vector-based models are typically directed at representing words in isolation and thus best suited for measuring similarity out of context. In his paper we propose a probabilistic framework for measuring similarity in context. Central to our approach is the intuition that word meaning is represented as a probability distribution over a set of latent senses and is modulated by context. Experimental results on lexical substitution and word similarity show that our algorithm outperforms previously proposed models.