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....
| Publicado en: | Federal Probation Vol. 87; no. 3; pp. 19 - 27 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Superintendent of Documents
Dec2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=179079901&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 179079901 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00149128 FEP jtl: Federal Probation issn: 00149128 maglogo: N pubinfo: dt: Dec2023 vid: 87 iid: 3 pid: 372 pub: Superintendent of Documents artinfo: ui: 179079901 ppf: 19 ppct: 8 formats: fmt: @attributes: type: T tig: atl: Exploring Probation and Parole Records Using Natural Language Processing: A Case Study of Supervisory Condition Notes. aug: au: Elyazori, Hadeel Thurman, Teneshia Lybarger, Kevin Taxman, Faye S. su: Mental health services Domestic violence Machine learning Language models Natural language processing Data mining Drivers' licenses Electronic health records Curfews sug: subj: Mental health services Domestic violence Offices of Mental Health Practitioners (except Physicians) Residential Mental Health and Substance Abuse Facilities Psychiatric and Substance Abuse Hospitals Regulation and Administration of Transportation Programs Machine learning Language models Natural language processing Data mining Drivers' licenses Electronic health records Curfews ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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