Capturing and evaluating higher order relations in word embeddings using tensor factorization

Bailey, Eric.

2017

Description
  • Abstract: In Natural Language Processing, most popular word embeddings involve low-rank factorization of a word co-occurrence based matrix. We aim to generalize this trend by studying word embeddings given by low-rank factorization of word co-occurrence based higher-order arrays, or tensors. We present four novel word embeddings based on tensor factorization and show they outperform popular s... read more
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qz20t4633
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tufts:22376
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