Document categorization in legal electronic discovery: computer classification vs. manual review
By Herbert L. Roitblat, Anne Kershaw, Patrick Oot. 2010
93 citations recorded by the source corpus.
In litigation in the US, the parties are obligated to produce to one another, when requested, those documents that are potentially relevant to issues and facts of the litigation (called “discovery”). As the volume of electronic documents continues to grow, the expense of dealing with this obligation threatens to surpass the amounts at issue and the time to identify these relevant documents can delay a case for months or years.The same holds true for government investigations and third-parties served with subpoenas. As a result, litigants are looking for ways to reduce the time and expense of discovery. One approach is to supplant or reduce the traditional means of having people, usually attorneys, read each document, with automated procedures that use information retrieval and machine categorization to identify the relevant documents. This study compared an original categorization, obtained as part of a response to a Department of Justice Request and produced by having one or more of 225 attor-neys review each document with automated categorization systems provided by two legal service providers.The goal was to determine whether the automated systems could categorize documents at least as well as human reviewers could, thereby saving time and expense. The results support the idea that machine categorization is no less accurate at identifying relevant/responsive documents than employing a team of reviewers. Based on these results, it would appear that using machine categorization can be a reasonable substitute for human review.
Subfields: Information Systems, Law, Political Science and International Relations, Information Retrieval & Search, Natural Language Processing
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In Information Systems
In Information Retrieval & Search
In Natural Language Processing
- Sebastian Ruder
- Jeremy Howard
- Hinrich Schütze
- Quoc V. Le
- Sandeep Attree
- Ivan Vulic
- Richard Socher
- Caiming Xiong
Citations in the corpus
- Universal Language Model Fine-tuning for Text Classification (2018, background): “…Language Processing (NLP) tasks with real-world applications such as spam, fraud, and bot detection (Jindal and Liu, 2007; Ngai et al., 2011; Chu et al., 2012), emergency response (Caragea et al., 2011), and commercial document classification, such as for legal discovery (Roitblat et al., 2010).”
- Fine-tuned Language Models for Text Classification (2018, background): “…(NLP) tasks with many important real-world applications such as spam, fraud, and bot detection (Jindal and Liu, 2007; Ngai et al., 2011; Chu et al., 2012), emergency response (Caragea et al., 2011), and commercial document classification, such as for legal discovery (Roitblat et al., 2010).”