21 citations · 21 across the 4 of their papers we have counts for
4 papers · 1 filter
Improving Semantic Composition with Offset Inference
Thomas Kober, Julie Weeds, Jeremy Reffin +1
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 Anc…
One Representation per Word - Does it make Sense for Composition?
Thomas Kober, Julie Weeds, John Wilkie +2
In this paper, we investigate whether an a priori disambiguation of word senses is strictly necessary or whether the meaning of a word in context can be disambiguated through compo…
Aligning Packed Dependency Trees: a theory of composition for distributional semantics
David Weir, Julie Weeds, Jeremy Reffin +1
We present a new framework for compositional distributional semantics in which the distributional contexts of lexemes are expressed in terms of anchored packed dependency trees. We…
Improving Sparse Word Representations with Distributional Inference for Semantic Composition
Thomas Kober, Julie Weeds, Jeremy Reffin +1
Distributional models are derived from co-occurrences in a corpus, where only a small proportion of all possible plausible co-occurrences will be observed. This results in a very s…