Semantically-informed domain adaptation for named entity recognition

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.

Bibliographic Details

Main Authors:	Borovikova, Mariya, Ferré, Arnaud, Bossy, Robert, Roche, Mathieu, Nédellec, Claire
Format:	conference_item biblioteca
Language:	eng
Published:	Springer
Online Access:	http://agritrop.cirad.fr/609693/ http://agritrop.cirad.fr/609693/1/Borovikova_et_al_ISMIS2024.pdf
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