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Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words.
A Bayesian analysis of some nonparametric problems
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Inducing word senses to improve web search result clustering
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The Westbury lab Wikipedia corpus
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Word representations: A simple and general method for semi-supervised learning
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Improving word representations via global context and multiple word prototypes
Huang, E. H., Socher, R., Manning, C. D., and Ng, A. Y · 2012
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Clustering and diversifying web search results with graph-based word sense induction
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Semeval-2013 task 13: Word sense induction for graded and non-graded senses
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SemEval-2013 task 11: Word sense induction and disambiguation within an end-user application
Navigli, R. and Vannella, D · 2013
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A unified model for word sense representation and disambiguation
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Efficient non-parametric estimation of multiple embeddings per word in vector space
Neelakantan, A., Shankar, J., Passos, A., and McCallum, A · 2014
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Learning word representation considering proximity and ambiguity
Qiu, L., Cao, Y., Nie, Z., Yu, Y., and Rui, Y · 2014
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Stochastic variational inference
Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J
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Linguistic regularities in continuous space word representations
Mikolov, T., Yih, W., and Zweig, G
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A mixture model with sharing for lexical semantics
Reisinger, J. and Mooney, R
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Multi-prototype vector-space models of word meaning
Reisinger, J. and Mooney, R. J
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A probabilistic model for learning multi-prototype word embeddings
Tian, F., Dai, H., Bian, J., Gao, B., Zhang, R., Chen, E., and Liu, T.-Y · 2014
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