Learning slowly to learn better : curriculum learning for legal ontology population

Ponencia presentada Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference

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Autores principales: Cardellino, Cristian, Teruel, Milagro, Alonso Alemany, Laura, Villata, Serena
Formato: conferenceObject
Lenguaje:Inglés
Publicado: 2024
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Acceso en línea:http://hdl.handle.net/11086/552703
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spelling I10-R141-11086-5527032024-07-12T06:33:22Z Learning slowly to learn better : curriculum learning for legal ontology population Cardellino, Cristian Teruel, Milagro Alonso Alemany, Laura Villata, Serena Ontologías Procesamiento del lenguaje natural Informática legal Deep learning Ponencia presentada Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference Fil: Cardellino, Cristian. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina. Fil: Teruel, Milagro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina. Fil: Alonso Alemany, Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina. Fil: Alonso Alemany, Laura. Universite Cote d’Azur; France. In this paper, we present an ontology population approach for legal ontologies. We exploit Wikipedia as a source of manually annotated examples of legal entities. We align YAGO, a Wikipedia-based ontology, and LKIF, an ontology specifically designed for the legal domain. Through this alignment, we can effectively populate the LKIF ontology, with the aim to obtain examples to train a Named Entity Recognizer and Classifier to be used for finding and classifying entities in legal texts. Since examples of annotated data in the legal domain are very few, we apply a machine learning strategy called curriculum learning aimed to overcome problems of overfitting by learning increasingly more complex concepts. We compare the performance of this method to identify Named Entities with respect to batch learning as well as two other baselines. Results are satisfying and foster further research in this direction. https://aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/view/15526 Fil: Cardellino, Cristian. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina. Fil: Teruel, Milagro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina. Fil: Alonso Alemany, Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina. Fil: Alonso Alemany, Laura. Universite Cote d’Azur; France. Otras Ciencias de la Computación e Información 2024-07-11T18:57:49Z 2024-07-11T18:57:49Z 2017 conferenceObject 978-157735787-2 http://hdl.handle.net/11086/552703 eng Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Electrónico y/o Digital
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Ontologías
Procesamiento del lenguaje natural
Informática legal
Deep learning
spellingShingle Ontologías
Procesamiento del lenguaje natural
Informática legal
Deep learning
Cardellino, Cristian
Teruel, Milagro
Alonso Alemany, Laura
Villata, Serena
Learning slowly to learn better : curriculum learning for legal ontology population
topic_facet Ontologías
Procesamiento del lenguaje natural
Informática legal
Deep learning
description Ponencia presentada Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference
format conferenceObject
author Cardellino, Cristian
Teruel, Milagro
Alonso Alemany, Laura
Villata, Serena
author_facet Cardellino, Cristian
Teruel, Milagro
Alonso Alemany, Laura
Villata, Serena
author_sort Cardellino, Cristian
title Learning slowly to learn better : curriculum learning for legal ontology population
title_short Learning slowly to learn better : curriculum learning for legal ontology population
title_full Learning slowly to learn better : curriculum learning for legal ontology population
title_fullStr Learning slowly to learn better : curriculum learning for legal ontology population
title_full_unstemmed Learning slowly to learn better : curriculum learning for legal ontology population
title_sort learning slowly to learn better : curriculum learning for legal ontology population
publishDate 2024
url http://hdl.handle.net/11086/552703
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AT teruelmilagro learningslowlytolearnbettercurriculumlearningforlegalontologypopulation
AT alonsoalemanylaura learningslowlytolearnbettercurriculumlearningforlegalontologypopulation
AT villataserena learningslowlytolearnbettercurriculumlearningforlegalontologypopulation
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