Dynamic Named Entity Recognition

Tristan Luiggi , Vincent Guigue (1) , Laure Soulier (2, 3) , Siwar Jendoubi , Aurelien Baelde

1 MIA Paris-Saclay - Mathématiques et Informatique Appliquées
2 MLIA - Machine Learning and Information Access
3 STL - Sciences et Technologies des Langues - LISN

Named Entity Recognition (NER) is a challenging and widely studied task that involves detecting and typing entities in text. So far, NER still approaches entity typing as a task of classification into universal classes (e.g. date, person, or location). Recent advances in natural language processing focus on architectures of increasing complexity that may lead to overfitting and memorization, and thus, underuse of context. Our work targets situations where the type of entities depends on the context and cannot be solved solely by memorization. We hence introduce a new task: Dynamic Named Entity Recognition (DNER), providing a framework to better evaluate the ability of algorithms to extract entities by exploiting the context. The DNER benchmark is based on two datasets, DNER-RotoWire and DNER-IMDb. We evaluate baseline models and present experiments reflecting issues and research axes related to this novel task.

Mots clés: Information extraction; NER; contextualization; datasets

Domaines: Réseau de neurones [cs.NE]

Tristan Luiggi, Vincent Guigue, Laure Soulier, Siwar Jendoubi, Aurelien Baelde. Dynamic Named Entity Recognition. 38th ACM/SIGAPP Symposium on Applied Computing, Mar 2023, Tallinn, Estonia. pp.890-897, ⟨10.1145/3555776.3577603⟩. ⟨hal-04284318⟩