Analysing Predictive Coding Algorithms for Document Review
Lawsuits and regulatory investigations in today's legal environment demand corporations to engage in increasingly intense data-focused engagements to find, acquire, and evaluate vast amounts of data. In recent years, technology-assisted review (TAR) has become a more crucial part of the documen...
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| Published in: | International journal for research in applied science and engineering technology Vol. 9; no. 11; pp. 1679 - 1681 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
30.11.2021
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| ISSN: | 2321-9653, 2321-9653 |
| Online Access: | Get full text |
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| Summary: | Lawsuits and regulatory investigations in today's legal environment demand corporations to engage in increasingly intense data-focused engagements to find, acquire, and evaluate vast amounts of data. In recent years, technology-assisted review (TAR) has become a more crucial part of the document review process in legal discovery. Attorneys now have been using machine learning techniques like text classification to identify responsive information. In the legal domain, text classification is referred to as predictive coding or technology assisted review (TAR). Predictive coding is used to increase the number of relevant documents identified, while reducing human labelling efforts and manual review of documents. Deep learning models mixed with word embeddings have demonstrated to be more effective in predictive coding in recent years. Deep learning models, on the other hand, have a lot of variables, making it difficult and time-consuming for legal professionals to choose the right settings. In this paper, we will look at a few predictive coding algorithms and discuss which one is the most efficient among them. Keywords: Technology-assisted-review, predictive coding, machine learning, text classification, deep learning, CNN , Unscented Kalman Filter, Logistic Regression, SVM |
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| ISSN: | 2321-9653 2321-9653 |
| DOI: | 10.22214/ijraset.2021.39076 |