2018
EMNLP
EMNLP 2018
Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning
Abstract
AbstractThis 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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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— legal text
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Application Areas > Domain Adaptation
Natural Language Processing > Applications > Text Classification
Machine Learning > Learning Paradigms > Transfer Learning
Machine Learning > Learning Types > Transfer Learning
Natural Language Processing > Applications > Sentiment Analysis