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Improving Semantic Composition with Offset Inference (1704.06692v1)

Published 21 Apr 2017 in cs.CL

Abstract: Count-based distributional semantic models suffer from sparsity due to unobserved but plausible co-occurrences in any text collection. This problem is amplified for models like Anchored Packed Trees (APTs), that take the grammatical type of a co-occurrence into account. We therefore introduce a novel form of distributional inference that exploits the rich type structure in APTs and infers missing data by the same mechanism that is used for semantic composition.

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Authors (4)
  1. Thomas Kober (12 papers)
  2. Julie Weeds (11 papers)
  3. Jeremy Reffin (5 papers)
  4. David Weir (15 papers)
Citations (4)

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