@inproceedings{salaka-etal-2018-fast,
title = "Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning",
author = "Salaka, Viraj and
Warushavithana, Menuka and
de Silva, Nisansa and
Perera, Amal Shehan and
Ratnayaka, Gathika and
Rupasinghe, Thejan",
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
Hoste, Veronique and
Klinger, Roman",
booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6238",
doi = "10.18653/v1/W18-6238",
pages = "260--265",
abstract = "This study proposes a novel way of identifying the sentiment of the phrases used in the legal domain. The added complexity of the language used in law, and the inability of the existing systems to accurately predict the sentiments of words in law are the main motivations behind this study. This is a transfer learning approach which can be used for other domain adaptation tasks as well. The proposed methodology achieves an improvement of over 6{\%} compared to the source model{'}s accuracy in the legal domain.",
}
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%0 Conference Proceedings
%T Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning
%A Salaka, Viraj
%A Warushavithana, Menuka
%A de Silva, Nisansa
%A Perera, Amal Shehan
%A Ratnayaka, Gathika
%A Rupasinghe, Thejan
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F salaka-etal-2018-fast
%X This study proposes a novel way of identifying the sentiment of the phrases used in the legal domain. The added complexity of the language used in law, and the inability of the existing systems to accurately predict the sentiments of words in law are the main motivations behind this study. This is a transfer learning approach which can be used for other domain adaptation tasks as well. The proposed methodology achieves an improvement of over 6% compared to the source model’s accuracy in the legal domain.
%R 10.18653/v1/W18-6238
%U https://aclanthology.org/W18-6238
%U https://doi.org/10.18653/v1/W18-6238
%P 260-265
Markdown (Informal)
[Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning](https://aclanthology.org/W18-6238) (Salaka et al., WASSA 2018)
ACL