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 / Palmero Aprosio, Alessio; Paccosi, Teresa. - 3473:(2023). (Intervento presentato al convegno 8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2023 tenutosi a Parma, Italy nel 7th-8th September 2023).
NERMuD at EVALITA 2023: Overview of the Named-Entities Recognition on Multi-Domain Documents Task
Palmero Aprosio, Alessio;Paccosi, Teresa
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.File | Dimensione | Formato | |
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