77 citations · 353 across the 60 of their papers we have counts for
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Distilling Relation Embeddings from Pre-trained Language Models
Asahi Ushio, Jose Camacho-Collados, Steven Schockaert
Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual…
Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection
Yixiao Wang, Zied Bouraoui, Luis Espinosa Anke +1
One of the long-standing challenges in lexical semantics consists in learning representations of words which reflect their semantic properties. The remarkable success of word embed…
Probing Pre-Trained Language Models for Disease Knowledge
Israa Alghanmi, Luis Espinosa-Anke, Steven Schockaert
Pre-trained language models such as ClinicalBERT have achieved impressive results on tasks such as medical Natural Language Inference. At first glance, this may suggest that these…
BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?
Asahi Ushio, Luis Espinosa-Anke, Steven Schockaert +1
Analogies play a central role in human commonsense reasoning. The ability to recognize analogies such as "eye is to seeing what ear is to hearing", sometimes referred to as analogi…