Named entities – typically expressed via proper nouns – play a key role in Natural Language Processing, as their identification and comprehension are crucial in tasks such as Relation Extraction, Coreference Resolution and Question Answering, among others. Tasks like these also often entail dealing with concepts – typically represented by common nouns – which, however, have not received as much attention. Indeed, the potential of their identification and understanding remains underexplored, as does the benefit of a synergistic formulation with named entities. To fill this gap, we introduce Concept and Named Entity Recognition (CNER), a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. We put forward a comprehensive set of categories that can be used to model concepts and named entities jointly, and propose new approaches for the creation of CNER datasets. We evaluate the benefits of performing CNER as a unified task extensively, showing that a CNER model gains up to +5.4 and +8 macro F1 points when compared to specialized named entity and concept recognition systems, respectively. Finally, to encourage the development of CNER systems, we release our datasets and models at https://github.com/Babelscape/cner.

CNER: Concept and Named Entity Recognition / Martinelli, Giuliano; Molfese, Francesco; Tedeschi, Simone; Fernández-Castro, Alberte; Navigli, Roberto. - Volume 1: Long Papers:(2024), pp. 8336-8351. (Intervento presentato al convegno North American Association for Computational Linguistics tenutosi a Mexico City; Mexico) [10.18653/V1/2024.NAACL-LONG.461].

CNER: Concept and Named Entity Recognition

Giuliano Martinelli
Primo
;
Francesco Molfese
Secondo
;
Simone Tedeschi
;
Roberto Navigli
Ultimo
2024

Abstract

Named entities – typically expressed via proper nouns – play a key role in Natural Language Processing, as their identification and comprehension are crucial in tasks such as Relation Extraction, Coreference Resolution and Question Answering, among others. Tasks like these also often entail dealing with concepts – typically represented by common nouns – which, however, have not received as much attention. Indeed, the potential of their identification and understanding remains underexplored, as does the benefit of a synergistic formulation with named entities. To fill this gap, we introduce Concept and Named Entity Recognition (CNER), a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. We put forward a comprehensive set of categories that can be used to model concepts and named entities jointly, and propose new approaches for the creation of CNER datasets. We evaluate the benefits of performing CNER as a unified task extensively, showing that a CNER model gains up to +5.4 and +8 macro F1 points when compared to specialized named entity and concept recognition systems, respectively. Finally, to encourage the development of CNER systems, we release our datasets and models at https://github.com/Babelscape/cner.
2024
North American Association for Computational Linguistics
information extraction; named entity recognition; natural language understanding
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
CNER: Concept and Named Entity Recognition / Martinelli, Giuliano; Molfese, Francesco; Tedeschi, Simone; Fernández-Castro, Alberte; Navigli, Roberto. - Volume 1: Long Papers:(2024), pp. 8336-8351. (Intervento presentato al convegno North American Association for Computational Linguistics tenutosi a Mexico City; Mexico) [10.18653/V1/2024.NAACL-LONG.461].
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Note: DOI: 10.18653/v1/2024.naacl-long.461
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1717749
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