Evaluation of Named Entity Recognition in Historical Argentinian Documents

Autores
Darfe, Facundo; Xamena, Eduardo; Orozco, Carlos I.
Año de publicación
2022
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Research over historical text volumes can be performed by means of automatic tools that help historians achieve more abstract and aggregated points of view. Tasks such as Information Extraction or Text Mining can be performed more efficiently if Machine Learning models are employed. We propose the evaluation of different state-of-the-art models over a new dataset for Named Entity Recognition. The dataset was built over a History texts volume about General Güemes, a national Argentinian independence hero. The results show that some models perform better in terms of precision, recall and f1-score for most types of entities. Specifically, pretrained language models fine-tuned for this particular task show considerably higher performance than classical models based on word embeddings and other kinds of representations and models.Besides, statistical tests are provided to ensure the significance in the differences of the performance values attained. Hence, the contribution of this work is twofold, on the one hand a new corpus and dataset for Named Entity Recognition and a complete statistical assessment of performance values of state-of-the-art models over the generated dataset.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Named Entity Recognition and Classification
Argentinian History
Pretrained Language Models
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/151702

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spelling Evaluation of Named Entity Recognition in Historical Argentinian DocumentsDarfe, FacundoXamena, EduardoOrozco, Carlos I.Ciencias InformáticasNamed Entity Recognition and ClassificationArgentinian HistoryPretrained Language ModelsResearch over historical text volumes can be performed by means of automatic tools that help historians achieve more abstract and aggregated points of view. Tasks such as Information Extraction or Text Mining can be performed more efficiently if Machine Learning models are employed. We propose the evaluation of different state-of-the-art models over a new dataset for Named Entity Recognition. The dataset was built over a History texts volume about General Güemes, a national Argentinian independence hero. The results show that some models perform better in terms of precision, recall and f1-score for most types of entities. Specifically, pretrained language models fine-tuned for this particular task show considerably higher performance than classical models based on word embeddings and other kinds of representations and models.Besides, statistical tests are provided to ensure the significance in the differences of the performance values attained. Hence, the contribution of this work is twofold, on the one hand a new corpus and dataset for Named Entity Recognition and a complete statistical assessment of performance values of state-of-the-art models over the generated dataset.Sociedad Argentina de Informática e Investigación Operativa2022-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf98-109http://sedici.unlp.edu.ar/handle/10915/151702enginfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/270/221info:eu-repo/semantics/altIdentifier/issn/2451-7496info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:39:06Zoai:sedici.unlp.edu.ar:10915/151702Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:39:06.691SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Evaluation of Named Entity Recognition in Historical Argentinian Documents
title Evaluation of Named Entity Recognition in Historical Argentinian Documents
spellingShingle Evaluation of Named Entity Recognition in Historical Argentinian Documents
Darfe, Facundo
Ciencias Informáticas
Named Entity Recognition and Classification
Argentinian History
Pretrained Language Models
title_short Evaluation of Named Entity Recognition in Historical Argentinian Documents
title_full Evaluation of Named Entity Recognition in Historical Argentinian Documents
title_fullStr Evaluation of Named Entity Recognition in Historical Argentinian Documents
title_full_unstemmed Evaluation of Named Entity Recognition in Historical Argentinian Documents
title_sort Evaluation of Named Entity Recognition in Historical Argentinian Documents
dc.creator.none.fl_str_mv Darfe, Facundo
Xamena, Eduardo
Orozco, Carlos I.
author Darfe, Facundo
author_facet Darfe, Facundo
Xamena, Eduardo
Orozco, Carlos I.
author_role author
author2 Xamena, Eduardo
Orozco, Carlos I.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Named Entity Recognition and Classification
Argentinian History
Pretrained Language Models
topic Ciencias Informáticas
Named Entity Recognition and Classification
Argentinian History
Pretrained Language Models
dc.description.none.fl_txt_mv Research over historical text volumes can be performed by means of automatic tools that help historians achieve more abstract and aggregated points of view. Tasks such as Information Extraction or Text Mining can be performed more efficiently if Machine Learning models are employed. We propose the evaluation of different state-of-the-art models over a new dataset for Named Entity Recognition. The dataset was built over a History texts volume about General Güemes, a national Argentinian independence hero. The results show that some models perform better in terms of precision, recall and f1-score for most types of entities. Specifically, pretrained language models fine-tuned for this particular task show considerably higher performance than classical models based on word embeddings and other kinds of representations and models.Besides, statistical tests are provided to ensure the significance in the differences of the performance values attained. Hence, the contribution of this work is twofold, on the one hand a new corpus and dataset for Named Entity Recognition and a complete statistical assessment of performance values of state-of-the-art models over the generated dataset.
Sociedad Argentina de Informática e Investigación Operativa
description Research over historical text volumes can be performed by means of automatic tools that help historians achieve more abstract and aggregated points of view. Tasks such as Information Extraction or Text Mining can be performed more efficiently if Machine Learning models are employed. We propose the evaluation of different state-of-the-art models over a new dataset for Named Entity Recognition. The dataset was built over a History texts volume about General Güemes, a national Argentinian independence hero. The results show that some models perform better in terms of precision, recall and f1-score for most types of entities. Specifically, pretrained language models fine-tuned for this particular task show considerably higher performance than classical models based on word embeddings and other kinds of representations and models.Besides, statistical tests are provided to ensure the significance in the differences of the performance values attained. Hence, the contribution of this work is twofold, on the one hand a new corpus and dataset for Named Entity Recognition and a complete statistical assessment of performance values of state-of-the-art models over the generated dataset.
publishDate 2022
dc.date.none.fl_str_mv 2022-10
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info:eu-repo/semantics/altIdentifier/issn/2451-7496
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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