Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning
| Autor: | Thejan Rupasinghe, Gathika Ratnayaka, Nisansa de Silva, Amal Shehan Perera, Viraj Salaka, Menuka Warushavithana |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Computer science business.industry 02 engineering and technology 010501 environmental sciences computer.software_genre Legal domain 01 natural sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business computer Computation and Language (cs.CL) Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | WASSA@EMNLP |
| DOI: | 10.48550/arxiv.1810.01912 |
| Popis: | 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. Comment: 9 pages, 3 figures |
| Databáze: | OpenAIRE |
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