264 citations · 429 across the 7 of their papers we have counts for
6 papers · 1 filter
Biomedical Concept Relatedness -- A large EHR-based benchmark
Claudia Schulz, Josh Levy-Kramer, Camille Van Assel +2
A promising application of AI to healthcare is the retrieval of information from electronic health records (EHRs), e.g. to aid clinicians in finding relevant information for a cons…
Correlations between Word Vector Sets
Vitalii Zhelezniak, April Shen, Daniel Busbridge +2
Similarity measures based purely on word embeddings are comfortably competing with much more sophisticated deep learning and expert-engineered systems on unsupervised semantic text…
Neural Language Priors
Joseph Enguehard, Dan Busbridge, Vitalii Zhelezniak +1
The choice of sentence encoder architecture reflects assumptions about how a sentence's meaning is composed from its constituent words. We examine the contribution of these archite…
Correlation Coefficients and Semantic Textual Similarity
Vitalii Zhelezniak, Aleksandar Savkov, April Shen +1
A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals…
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
Vitalii Zhelezniak, Aleksandar Savkov, April Shen +3
Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks. Furthermore, wh…
Offline bilingual word vectors, orthogonal transformations and the inverted softmax
Samuel L. Smith, David H. P. Turban, Steven Hamblin +1
Usually bilingual word vectors are trained "online". Mikolov et al. showed they can also be found "offline", whereby two pre-trained embeddings are aligned with a linear transforma…