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arXiv · 1704.06692

Improving Semantic Composition with Offset Inference

Thomas Kober·Julie Weeds·Jeremy Reffin·David Weir
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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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Thomas Kober, Julie Weeds, Jeremy Reffin, David Weir. 2017-04-21. Improving Semantic Composition with Offset Inference. https://arxiv.org/abs/1704.06692

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