Exploring Probation and Parole Records Using Natural Language Processing: A Case Study of Supervisory Condition Notes.

This research article explores the use of natural language processing (NLP) techniques to analyze probation and parole case notes. The authors demonstrate the effectiveness of NLP-based information extraction techniques in converting unstructured case notes into a structured semantic representation....

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Detalles Bibliográficos
Publicado en:	Federal Probation Vol. 87; no. 3; pp. 19 - 27
Autores principales:	Elyazori, Hadeel, Thurman, Teneshia, Lybarger, Kevin, Taxman, Faye S.
Formato:	Artículo
Publicado:	Superintendent of Documents Dec2023
Materias:	Mental health services Domestic violence Machine learning Language models Natural language processing Data mining Drivers' licenses Electronic health records Curfews
Acceso en línea:	Ver este registro en EBSCOhost

Descripción
Sumario:	This research article explores the use of natural language processing (NLP) techniques to analyze probation and parole case notes. The authors demonstrate the effectiveness of NLP-based information extraction techniques in converting unstructured case notes into a structured semantic representation. They use machine learning algorithms and large language models to extract information from client records and show the potential for using these techniques in data analytics within the criminal justice system. The article also discusses the performance of different models in analyzing probation and parole data, highlighting the advantages of language models over traditional machine learning models. The study acknowledges the limitations and the need for further research in applying information extraction techniques more broadly in correctional system data.