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Week 3 Reading

In this article, Kevin D. Ashley will discusses legal reasoning and training.
a person and a laptop
© AndreaPiacquadio (Pexel)
In the upcoming steps, we will talk about legal reasoning and training data. Before we start this, please read the abstract below taken from the article: “Automatically Extracting Meaning From Legal Texts: Opportunities and Challenges”.


Abstract

This paper surveys three basic legal-text analytic techniques—ML, network diagrams, and question answering (QA)—and illustrates how some currently available commercial applications employ or combine them. It then examines how well the text analytic techniques can answer legal questions given some inherent limitations in the technology. In more detail, ML refers to computer programs that use statistical means to induce or learn models from data with which they can classify a document or predict an outcome for a new case. Predictive coding techniques employed in e-discovery have already introduced ML from text into law firms. Network diagrams graph the relations between objects and can assist in making legal information retrieval smarter. The objects may be legal cases, statutory provisions, reference concepts, or communications nodes. Finally, QA systems search large text collections to locate texts or parts of texts that directly answer a user’s question. IBM’s Jeopardy-game-winning Watson program is, perhaps, the most famous example of a QA system.


If you are interested in this topic, you can get the full article below.

Note that while reading the full article is optional, we recommend that you read pages 1121, 1122, 1123, 1127, 1134, 1135, 1137, 1138, 1139, as they will help you to answer questions in the later quiz and final test in the course.

Kevin D. Ashley, Automatically Extracting Meaning from Legal Texts: Opportunities and Challenges, 35 GA. ST. U. L. REV. 1117 (2019), available at https://readingroom.law.gsu.edu/cgi/viewcontent.cgi?article=2976&context=gsulr

© Kevin D. Ashley
This article is from the free online

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