@inproceedings{percin-etal-2022-combining,
title = "Combining {W}ord{N}et and Word Embeddings in Data Augmentation for Legal Texts",
author = "Per{\c{c}}in, Sezen and
Galassi, Andrea and
Lagioia, Francesca and
Ruggeri, Federico and
Santin, Piera and
Sartor, Giovanni and
Torroni, Paolo",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preoțiuc-Pietro, Daniel",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nllp-1.4/",
doi = "10.18653/v1/2022.nllp-1.4",
pages = "47--52",
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."
}
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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.</abstract>
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%0 Conference Proceedings
%T Combining WordNet and Word Embeddings in Data Augmentation for Legal Texts
%A Perçin, Sezen
%A Galassi, Andrea
%A Lagioia, Francesca
%A Ruggeri, Federico
%A Santin, Piera
%A Sartor, Giovanni
%A Torroni, Paolo
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%S Proceedings of the Natural Legal Language Processing Workshop 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F percin-etal-2022-combining
%X 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.
%R 10.18653/v1/2022.nllp-1.4
%U https://aclanthology.org/2022.nllp-1.4/
%U https://doi.org/10.18653/v1/2022.nllp-1.4
%P 47-52
Markdown (Informal)
[Combining WordNet and Word Embeddings in Data Augmentation for Legal Texts](https://aclanthology.org/2022.nllp-1.4/) (Perçin et al., NLLP 2022)
ACL