Learning Slowly To Learn Better: Curriculum Learning for Legal Ontology Population

Cristian Cardellino 1 Milagro Teruel 1 Laura Alemany 1 Serena Villata 2
1 Universidad Nacional de Córdoba [Argentina]
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : 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.
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Communication dans un congrès
Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017), 2017, Marco Island, Florida, United States. Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017)
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https://hal.archives-ouvertes.fr/hal-01572442
Contributeur : Serena Villata <>
Soumis le : lundi 7 août 2017 - 13:45:43
Dernière modification le : mercredi 4 octobre 2017 - 16:08:21

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Cristian Cardellino, Milagro Teruel, Laura Alemany, Serena Villata. Learning Slowly To Learn Better: Curriculum Learning for Legal Ontology Population. Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017), 2017, Marco Island, Florida, United States. Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017). 〈hal-01572442〉

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