In this paper, we describe NERMuD, a Named-Entities Recognition (NER) shared task presented at the EVALITA 2023 evaluation campaign. NERMuD is organized into two different sub-tasks: a domain-agnostic classification and a domainspecific one. We display the evaluation of the system presented by the only task participant, ExtremITA. ExtremITA proposes a unified approach for all the tasks of EVALITA 2023, and it addresses in our case only the domain-agnostic sub-task. We present an updated version of KIND, the dataset distributed for the training of the system. We then provide the baselines proposed, the results of the evaluation, and a brief discussion.

NERMuD at EVALITA 2023: Overview of the Named-Entities Recognition on Multi-Domain Documents Task

Alessio Palmero Aprosio;Teresa Paccosi
2023-01-01

Abstract

In this paper, we describe NERMuD, a Named-Entities Recognition (NER) shared task presented at the EVALITA 2023 evaluation campaign. NERMuD is organized into two different sub-tasks: a domain-agnostic classification and a domainspecific one. We display the evaluation of the system presented by the only task participant, ExtremITA. ExtremITA proposes a unified approach for all the tasks of EVALITA 2023, and it addresses in our case only the domain-agnostic sub-task. We present an updated version of KIND, the dataset distributed for the training of the system. We then provide the baselines proposed, the results of the evaluation, and a brief discussion.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/341014
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