@inproceedings{cardellino-etal-2017-legal,
title = "Legal {NERC} with ontologies, {W}ikipedia and curriculum learning",
author = "Cardellino, Cristian and
Teruel, Milagro and
Alonso Alemany, Laura and
Villata, Serena",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2041",
pages = "254--259",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Legal NERC with ontologies, Wikipedia and curriculum learning
%A Cardellino, Cristian
%A Teruel, Milagro
%A Alonso Alemany, Laura
%A Villata, Serena
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F cardellino-etal-2017-legal
%X 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.
%U https://aclanthology.org/E17-2041
%P 254-259
Markdown (Informal)
[Legal NERC with ontologies, Wikipedia and curriculum learning](https://aclanthology.org/E17-2041) (Cardellino et al., EACL 2017)
ACL
- Cristian Cardellino, Milagro Teruel, Laura Alonso Alemany, and Serena Villata. 2017. Legal NERC with ontologies, Wikipedia and curriculum learning. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 254–259, Valencia, Spain. Association for Computational Linguistics.