Fine-grained legal entity annotation: A case study on the Brazilian Supreme Court |
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| Institution: | 1. School of Economics and Management, Harbin Engineering University, Harbin 150001, China;2. Management School, Harbin University of Commerce, Harbin 150028, China;3. Department of Computer Science and Information Engineering, Asia University, Taichung, 41354, Taiwan;4. Department of Computer Science and Engineering, Kyung Hee University, Republic of Korea;1. Business School, Hohai University, Nanjing 211100, China;2. Foreign Language School, Hohai University, Nanjing 211100, China;1. Department of Information Science and Technology, South China Business College, Guangdong University of Foreign Studies, Guangzhou 510545, China;2. Department of Computer and Information Science, University of Macau, Macau 999078, China;3. Department of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, China |
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| Abstract: | The exploration of legal documents in the Brazilian Judiciary context lacks reliable annotated corpus to support the development of new Natural Language Process (NLP) applications. Therefore, this paper presents a step toward exploring legal decisions with Named Entity Recognition (NER) in the Brazilian Supreme Court (STF) context. We aim to present a case study on the fine-grained annotation task of legal decisions, performed by law students as annotators where two levels of nested legal entities were annotated. Nested entities mapped in a preliminary study composed of four coarser legal named entities and twenty-four nested ones (fine-grained). The final result is a corpus of 594 decisions published by the STF annotated by the 76 law students, those with the highest average inter-annotator agreement score. We also present two baselines for NER based on Conditional Random Fields (CRFs) and Bidirectional Long-Short Term Memory Networks (BiLSTMs). This corpus is the first of its kind, the most extensive corpus known in Portuguese dedicated for legal named entity recognition, open and available to better support further research studies in a similar context. |
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| Keywords: | Named Entity Recognition Legal documents Manual annotation task Annotated corpus in Portuguese Brazilian Supreme Court |
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