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Conference Papers Year : 2017

Legal NERC with ontologies, Wikipedia and curriculum learning


In this paper, we present a Wikipedia-based approach to develop resources for the legal domain. We establish a mapping between a legal domain ontology, LKIF (Hoekstra et al., 2007), and a Wikipedia-based ontology, YAGO (Suchanek et al., 2007), and through that we populate LKIF. Moreover, we use the mentions of those entities in Wikipedia text to train a specific Named Entity Recognizer and Classifier. We find that this classifier works well in the Wikipedia, but, as could be expected, performance decreases in a corpus of judgments of the European Court of Human Rights. However, this tool will be used as a preprocess for human annotation. We resort to a technique called curriculum learning aimed to overcome problems of overfitting by learning increasingly more complex concepts. However, we find that in this particular setting, the method works best by learning from most specific to most general concepts, not the other way round.
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hal-01572444 , version 1 (07-08-2017)



Cristian Cardellino, Milagro Teruel, Laura Alonso Alemany, Serena Villata. Legal NERC with ontologies, Wikipedia and curriculum learning. 15th European Chapter of the Association for Computational Linguistics (EACL 2017), 2017, Valencia, Spain. pp.254 - 259, ⟨10.18653/v1/E17-2041⟩. ⟨hal-01572444⟩
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