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Sentence Boundary Detection in Adjudicatory Decisions in the United States

NCJ Number
255267
Date Published
2017
Length
25 pages
Author(s)
Jeromor Savelka; Vern R. Walker; Matthias Grabmair; Kevin D. Ashley
Agencies
NIJ-Sponsored
Publication Type
Research (Applied/Empirical), Report (Study/Research), Report (Grant Sponsored), Program/Project Description
Grant Number(s)
2016-R2-CX-0010
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 Created: July 20, 2021