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Sentence Boundary Detection in Adjudicatory Decisions in the United States

NCJ Number
255267
Journal
Traitement Automatique Des Langues Volume: 58 Issue: 2 Dated: 2017 Pages: 21-45
Author(s)
Jaromir Savelka; Vern R. Walker; Matthias Grabmair; Kevin D. Ashley
Date Published
2017
Length
25 pages
Annotation

This article reports the results of an effort to enable computers to segment U.S. adjudicatory decisions into sentences.

Abstract

The project created a data set of 80 court decisions from four different domains. Findings indicate that legal decisions are more challenging for existing sentence boundary detection systems than for non-legal texts. Existing sentence boundary detection systems are based on a number of assumptions that do not hold for legal texts; hence their performance is impaired. The project indicates that a general statistical sequence labeling model is capable of learning the definition more efficiently. The project trained a number of conditional random fields models that outperform the traditional sentence boundary detection systems when applied to adjudicatory decisions. (publisher abstract modified)

Date Published: January 1, 2017