24 citations · 37 across the 11 of their papers we have counts for
5 papers · 1 filter
Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy
Na Li, Zied Bouraoui, Steven Schockaert
Ultra-fine entity typing (UFET) is the task of inferring the semantic types, from a large set of fine-grained candidates, that apply to a given entity mention. This task is especia…
Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models
Na Li, Hanane Kteich, Zied Bouraoui +1
Learning vectors that capture the meaning of concepts remains a fundamental challenge. Somewhat surprisingly, perhaps, pre-trained language models have thus far only enabled modest…
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…
Modeling Semantic Relatedness using Global Relation Vectors
Shoaib Jameel, Zied Bouraoui, Steven Schockaert
Word embedding models such as GloVe rely on co-occurrence statistics from a large corpus to learn vector representations of word meaning. These vectors have proven to capture surpr…