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Found 2 papers, 1 papers with code
LEARNING SEMANTIC WORD RESPRESENTATIONS VIA TENSOR FACTORIZATION
We present two new word embedding techniques based on tensor factorization and show that they outperform common methods on several semantic NLP tasks when given the same data.
Word Embeddings via Tensor Factorization
We show that embeddings based on tensor factorization can be used to discern the various meanings of polysemous words without being explicitly trained to do so, and motivate the intuition behind why this works in a way that doesn't with existing methods.
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