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By design, word embeddings are unable to model the dynamic nature of words' semantics, i.e., the property of words to correspond to potentially different meanings.
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Meaningful clustering of senses helps boost Word Sense Disambiguation performance
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Maria Pelevina, Nikolay Arefyev, Chris Biemann, and Alexander Panchenko. 2016 · 2016
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Mohammad Taher Pilehvar and Nigel Collier. 2016 · 2016
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Embedding words and senses together via joint knowledge-enhanced training
Massimiliano Mancini, Jose Camacho-Collados, Ignacio Iacobacci, and Roberto Navigli. 2017 · 2017
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Towards a Seamless Integration of Word Senses into Downstream NLP Applications
Mohammad Taher Pilehvar, Jose Camacho-Collados, Roberto Navigli, and Nigel Collier. 2017 · 2017
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Detecting asymmetric semantic relations in context: A case-study on hypernymy detection
Yogarshi Vyas and Marine Carpuat. 2017 · 2017
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From word to sense embeddings: A survey on vector representations of meaning
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013b
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