77 citations · 123 across the 16 of their papers we have counts for
16 papers · 1 filter
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…
Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings
Na Li, Zied Bouraoui, Jose Camacho Collados +3
While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, such vectors continue to play an imp…
Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities
Carla Pérez-Almendros, Luis Espinosa-Anke, Steven Schockaert
In this paper, we introduce a new annotated dataset which is aimed at supporting the development of NLP models to identify and categorize language that is patronizing or condescend…
Modelling Semantic Categories using Conceptual Neighborhood
Zied Bouraoui, Jose Camacho-Collados, Luis Espinosa-Anke +1
While many methods for learning vector space embeddings have been proposed in the field of Natural Language Processing, these methods typically do not distinguish between categorie…
Inducing Relational Knowledge from BERT
Zied Bouraoui, Jose Camacho-Collados, Steven Schockaert
One of the most remarkable properties of word embeddings is the fact that they capture certain types of semantic and syntactic relationships. Recently, pre-trained language models…