77 citations · 353 across the 60 of their papers we have counts for
33 papers · 1 filter
Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities
Hans Hergen Lehmann, Jae Hee Lee, Steven Schockaert +1
Large Language Models (LLMs) are increasingly used for knowledge-based reasoning tasks, yet understanding when they rely on genuine knowledge versus superficial heuristics remains…
Extracting Conceptual Spaces from LLMs Using Prototype Embeddings
Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
Conceptual spaces represent entities and concepts using cognitively meaningful dimensions, typically referring to perceptual features. Such representations are widely used in cogni…
Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis
Na Li, Thomas Bailleux, Zied Bouraoui +1
We consider the problem of finding plausible knowledge that is missing from a given ontology, as a generalisation of the well-studied taxonomy expansion task. One line of work trea…
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
Conceptual spaces represent entities in terms of their primitive semantic features. Such representations are highly valuable but they are notoriously difficult to learn, especially…
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…
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…
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…
Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings
Yerai Doval, Jose Camacho-Collados, Luis Espinosa-Anke +1
Word embeddings have become a standard resource in the toolset of any Natural Language Processing practitioner. While monolingual word embeddings encode information about words in…