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                            Library & Multimedia
 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)
      
  
          Additional Details
Grant Number(s)
        2016-R2-CX-0010


  
  
        
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  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description


  
  
        
  Award
        Recommendation System for Statutory Interpretation in Cybercrime
    
  
  
Date Published: January 1, 2017
    
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