Legal area classification: A comparative study of text classifiers on Singapore Supreme Court judgments
This paper conducts a comparative study on the performance of various machine learning(“ML”) approaches for classifying judgments into legal areas. Using a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how state-of-the-art NLP methods compare against traditional statistica...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2019
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sol_research/2956 https://ink.library.smu.edu.sg/context/sol_research/article/4914/viewcontent/A_Comparative_Study_of_Text_Classifiers_on_Singapore_Supreme_Court_Judgments.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | This paper conducts a comparative study on the performance of various machine learning(“ML”) approaches for classifying judgments into legal areas. Using a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how state-of-the-art NLP methods compare against traditional statistical models when applied to a legal corpus that comprised few but lengthy documents. All approaches tested, including topic model, word embedding, and language model-based classifiers, performed well with as little as a few hundred judgments. However, more work needs to be done to optimize state-of-the-art methods for the legal domain. |
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