Semantically-informed domain adaptation for named entity recognition

Borovikova Mariya, Ferré Arnaud, Bossy Robert, Roche Mathieu, Nédellec Claire. 2024. Semantically-informed domain adaptation for named entity recognition. In : Foundations of intelligent systems: 27th International Symposium, ISMIS 2024, Poitiers, France, June 17-19, 2024, Proceedings. Appice Annalisa (ed.), Azzag Hanane (ed.), Hacid Mohand-Said (ed.), Hadjali Allel (ed.), Ras Zbigniew (ed.). Cham : Springer, 55-64. (Lecture Notes in Artificial Intelligence, 14670) ISBN 978-3-031-62699-9 International Symposium on Methodologies for Intelligent Systems (ISMIS 2024). 27, Poitiers, France, 17 Juin 2024/19 Juin 2024.

Résumé : Named Entity Recognition (NER) is an important task in Natural Language Processing that involves identifying entities in unstructured text. State-of-the-art NER methods often require extensive manual labeling for training. To bridge this gap, this paper introduces a domain adaptation technique that leverages semantic information about entity types using Sentence-BERT embeddings of their textual descriptions. We conduct experiments across various datasets from both general and biological domains, evaluating our approach in standard and zero-shot settings. Our experiences demonstrate the effectiveness of our method, which outperforms existing zero-shot techniques on certain datasets. Our findings underscore the importance of accurate semantic representations for entity types. This paper contributes to the advancement of zero-shot domain adaptation for NER and opens avenues for future research in improving NER systems' adaptability and performance across diverse domains.

Mots-clés libres : Natural Language Processing, Language Model, Named entity recognition, Domain adaptation

Agences de financement hors UE : Agence Nationale de la Recherche

Projets sur financement : (FRA) Building epidemiological surveillance and prophylaxis with observations both near and distant

Auteurs et affiliations

• Borovikova Mariya, Université Paris-Saclay (FRA)
• Ferré Arnaud, Université Paris-Saclay (FRA)
• Bossy Robert, INRAE (FRA)
• Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568
• Nédellec Claire, INRAE (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/609693/)

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