77 citations · 206 across the 39 of their papers we have counts for
6 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 Monotonic and Non-Monotonic Attribute Dependencies with Embeddings: A Theoretical Analysis
Steven Schockaert
During the last decade, entity embeddings have become ubiquitous in Artificial Intelligence. Such embeddings essentially serve as compact but semantically meaningful representation…
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning
Kun Yan, Zied Bouraoui, Ping Wang +2
Few-shot learning (FSL) is the task of learning to recognize previously unseen categories of images from a small number of training examples. This is a challenging task, as the ava…
A Description Logic for Analogical Reasoning
Steven Schockaert, Yazmín Ibáñez-García, Víctor Gutiérrez-Basulto
Ontologies formalise how the concepts from a given domain are interrelated. Despite their clear potential as a backbone for explainable AI, existing ontologies tend to be highly in…
Few-shot Image Classification with Multi-Facet Prototypes
Kun Yan, Zied Bouraoui, Ping Wang +2
The aim of few-shot learning (FSL) is to learn how to recognize image categories from a small number of training examples. A central challenge is that the available training exampl…