Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning

Viraj Salaka, Menuka Warushavithana, Nisansa de Silva, Amal Shehan Perera, Gathika Ratnayaka, Thejan Rupasinghe


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.
Anthology ID:
W18-6238
Volume:
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
October
Year:
2018
Address:
Brussels, Belgium
Editors:
Alexandra Balahur, Saif M. Mohammad, Veronique Hoste, Roman Klinger
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
260–265
Language:
URL:
https://aclanthology.org/W18-6238
DOI:
10.18653/v1/W18-6238
Bibkey:
Cite (ACL):
Viraj Salaka, Menuka Warushavithana, Nisansa de Silva, Amal Shehan Perera, Gathika Ratnayaka, and Thejan Rupasinghe. 2018. Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 260–265, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning (Salaka et al., WASSA 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-6238.pdf