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Word embedding is a powerful tool in natural language processing.
Some mathematical notes on three-mode factor analysis
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Semantic compositionality through recursive matrix-vector spaces
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Most tensor problems are np-hard
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Distributed representations of words and phrases and their compositionality
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Rand-walk: A latent variable model approach to word embeddings
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Syntax-aware multi-sense word embeddings for deep compositional models of meaning
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Word embedding revisited: A new representation learning and explicit matrix factorization perspective
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Learning adjective meanings with a tensor-based skip-gram model
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Tensorflow: A system for large-scale machine learning
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Dependency-based word embeddings
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Neural word embedding as implicit matrix factorization
Levy, O. and Goldberg, Y. (2014b)
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A simple but tough-to-beat baseline for sentence embeddings
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Word embeddings via tensor factorization
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Skip-gram-zipf+ uniform= vector additivity
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Advances in pre-training distributed word representations
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Orthogonalized als: A theoretically principled tensor decomposition algorithm for practical use
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