Search Results for author: François Remy

Found 8 papers, 2 papers with code

In-Context Learning for Extreme Multi-Label Classification

2 code implementations22 Jan 2024 Karel D'Oosterlinck, Omar Khattab, François Remy, Thomas Demeester, Chris Develder, Christopher Potts

Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it is generally infeasible to demonstrate every class in a prompt.

Classification Extreme Multi-Label Classification +2

BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights

no code implementations27 Nov 2023 François Remy, Kris Demuynck, Thomas Demeester

Our new multilingual model enables a range of languages to benefit from our advancements in biomedical semantic representation learning, opening a new avenue for bioinformatics researchers around the world.

Clinical Knowledge Contrastive Learning +2

BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance

1 code implementation22 May 2023 Karel D'Oosterlinck, François Remy, Johannes Deleu, Thomas Demeester, Chris Develder, Klim Zaporojets, Aneiss Ghodsi, Simon Ellershaw, Jack Collins, Christopher Potts

We introduce BioDEX, a large-scale resource for Biomedical adverse Drug Event Extraction, rooted in the historical output of drug safety reporting in the U. S. BioDEX consists of 65k abstracts and 19k full-text biomedical papers with 256k associated document-level safety reports created by medical experts.

Event Extraction

Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning

no code implementations11 May 2023 François Remy, Alfiya Khabibullina, Thomas Demeester

This paper shines a light on the potential of definition-based semantic models for detecting idiomatic and semi-idiomatic multiword expressions (MWEs) in clinical terminology.

Language Modelling Representation Learning

BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)

no code implementations21 Oct 2022 François Remy, Kris Demuynck, Thomas Demeester

This work introduces BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts.

Contrastive Learning text similarity

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