Dynamic Named Entity Recognition

• URL: http://arxiv.org/abs/2302.10314v1
• Date: Thu, 16 Feb 2023 15:50:02 GMT
• Title: Dynamic Named Entity Recognition
• Authors: Tristan Luiggi, Laure Soulier, Vincent Guigue, Siwar Jendoubi, Aur\'elien Baelde
• Abstract summary: We introduce a new task: Dynamic Named Entity Recognition (DNER) DNER provides a framework to better evaluate the ability of algorithms to extract entities by exploiting the context. We evaluate baseline models and present experiments reflecting issues and research axes related to this novel task.
• Score: 5.9401550252715865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 innatural 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.

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