Creating balanced labeled textual corpora for complex tasks, like legal analysis, is a challenging and expensive process that often requires the collaboration of domain experts. To address this problem, we propose a data augmentation method based on the combination of GloVe word embeddings and the WordNet ontology. We present an example of application in the legal domain, specifically on decisions of the Court of Justice of the European Union. Our evaluation with human experts confirms that our method is more robust than the alternatives.

Combining WordNet and Word Embeddings in Data Augmentation for Legal Texts

Andrea Galassi;Francesca Lagioia;Giovanni Sartor;Piera Santin;Federico Ruggeri;Paolo Torroni
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Abstract

Creating balanced labeled textual corpora for complex tasks, like legal analysis, is a challenging and expensive process that often requires the collaboration of domain experts. To address this problem, we propose a data augmentation method based on the combination of GloVe word embeddings and the WordNet ontology. We present an example of application in the legal domain, specifically on decisions of the Court of Justice of the European Union. Our evaluation with human experts confirms that our method is more robust than the alternatives.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
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Andrea Galassi, Francesca Lagioia, Giovanni Sartor, Piera Santin, Sezen Perçin, Federico Ruggeri, Paolo Torroni
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/905768
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