6 citations · 12 across the 10 of their papers we have counts for
12 papers
Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders
Qiwei Peng, David Weir, Julie Weeds
Previous works have demonstrated the effectiveness of utilising pre-trained sentence encoders based on their sentence representations for meaning comparison tasks. Though such repr…
Representing Syntax and Composition with Geometric Transformations
Lorenzo Bertolini, Julie Weeds, David Weir +1
The exploitation of syntactic graphs (SyGs) as a word's context has been shown to be beneficial for distributional semantic models (DSMs), both at the level of individual word repr…
Data Augmentation for Hypernymy Detection
Thomas Kober, Julie Weeds, Lorenzo Bertolini +1
The automatic detection of hypernymy relationships represents a challenging problem in NLP. The successful application of state-of-the-art supervised approaches using distributed r…
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…