2 citations · 4 across the 8 of their papers we have counts for
10 papers
Automated Query Generation for Evidence Collection from Web Search Engines
Nestor Prieto-Chavana, Julie Weeds, David Weir
It is widely accepted that so-called facts can be checked by searching for information on the Internet. This process requires a fact-checker to formulate a search query based on th…
Automatic Scoring of Dream Reports' Emotional Content with Large Language Models
Lorenzo Bertolini, Valentina Elce, Adriana Michalak +2
In the field of dream research, the study of dream content typically relies on the analysis of verbal reports provided by dreamers upon awakening from their sleep. This task is cla…
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…
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
Lena Schmidt, Julie Weeds, Julian P. T. Higgins
This research on data extraction methods applies recent advances in natural language processing to evidence synthesis based on medical texts. Texts of interest include abstracts of…
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…