77 citations · 353 across the 60 of their papers we have counts for
11 papers · 2 filters
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction
Frank Mtumbuka, Steven Schockaert
Relation extraction is essentially a text classification problem, which can be tackled by fine-tuning a pre-trained language model (LM). However, a key challenge arises from the fa…
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies
Amit Gajbhiye, Zied Bouraoui, Na Li +3
Concepts play a central role in many applications. This includes settings where concepts have to be modelled in the absence of sentence context. Previous work has therefore focused…
Solving Hard Analogy Questions with Relation Embedding Chains
Nitesh Kumar, Steven Schockaert
Modelling how concepts are related is a central topic in Lexical Semantics. A common strategy is to rely on knowledge graphs (KGs) such as ConceptNet, and to model the relation bet…
Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces
Usashi Chatterjee, Amit Gajbhiye, Steven Schockaert
The theory of Conceptual Spaces is an influential cognitive-linguistic framework for representing the meaning of concepts. Conceptual spaces are constructed from a set of quality d…
RelBERT: Embedding Relations with Language Models
Asahi Ushio, Jose Camacho-Collados, Steven Schockaert
Many applications need access to background knowledge about how different concepts and entities are related. Although Knowledge Graphs (KG) and Large Language Models (LLM) can addr…
Ragas: Automated Evaluation of Retrieval Augmented Generation
Shahul Es, Jithin James, Luis Espinosa-Anke +1
We introduce Ragas (Retrieval Augmented Generation Assessment), a framework for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines. RAG systems are compose…
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…
What's the Meaning of Superhuman Performance in Today's NLU?
Simone Tedeschi, Johan Bos, Thierry Declerck +9
In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks su…
A RelEntLess Benchmark for Modelling Graded Relations between Named Entities
Asahi Ushio, Jose Camacho Collados, Steven Schockaert
Relations such as "is influenced by", "is known for" or "is a competitor of" are inherently graded: we can rank entity pairs based on how well they satisfy these relations, but it…
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains
Frank Mtumbuka, Steven Schockaert
Entity typing is the task of assigning semantic types to the entities that are mentioned in a text. In the case of fine-grained entity typing (FET), a large set of candidate type l…