Persistent Link:
http://hdl.handle.net/10150/105786
Title:
Extracting Meaningful Entities from Police Narrative Reports
Author:
Chau, Michael; Xu, Jennifer J.; Chen, Hsinchun
Citation:
Extracting Meaningful Entities from Police Narrative Reports 2002-06,
Issue Date:
Jun-2002
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105786
Submitted date:
2004-08-16
Abstract:
Valuable criminal-justice data in free texts such as police narrative reports are currently difficult to be accessed and used by intelligence investigators in crime analyses. It would be desirable to automatically identify from text reports meaningful entities, such as person names, addresses, narcotic drugs, or vehicle names to facilitate crime investigation. In this paper, we report our work on a neural network-based entity extractor, which applies named-entity extraction techniques to identify useful entities from police narrative reports. Preliminary evaluation results demonstrated that our approach is feasible and has some potential values for real-life applications. Our system achieved encouraging precision and recall rates for person names and narcotic drugs, but did not perform well for addresses and personal properties. Our future work includes conducting larger-scale evaluation studies and enhancing the system to capture human knowledge interactively.
Type:
Conference Paper
Language:
en
Keywords:
Knowledge Management; Data Mining; Information Seeking Behaviors
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab; Extraction

Full metadata record

DC FieldValue Language
dc.contributor.authorChau, Michaelen_US
dc.contributor.authorXu, Jennifer J.en_US
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-08-16T00:00:01Z-
dc.date.available2010-06-18T23:34:22Z-
dc.date.issued2002-06en_US
dc.date.submitted2004-08-16en_US
dc.identifier.citationExtracting Meaningful Entities from Police Narrative Reports 2002-06,en_US
dc.identifier.urihttp://hdl.handle.net/10150/105786-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractValuable criminal-justice data in free texts such as police narrative reports are currently difficult to be accessed and used by intelligence investigators in crime analyses. It would be desirable to automatically identify from text reports meaningful entities, such as person names, addresses, narcotic drugs, or vehicle names to facilitate crime investigation. In this paper, we report our work on a neural network-based entity extractor, which applies named-entity extraction techniques to identify useful entities from police narrative reports. Preliminary evaluation results demonstrated that our approach is feasible and has some potential values for real-life applications. Our system achieved encouraging precision and recall rates for person names and narcotic drugs, but did not perform well for addresses and personal properties. Our future work includes conducting larger-scale evaluation studies and enhancing the system to capture human knowledge interactively.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectKnowledge Managementen_US
dc.subjectData Miningen_US
dc.subjectInformation Seeking Behaviorsen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherExtractionen_US
dc.titleExtracting Meaningful Entities from Police Narrative Reportsen_US
dc.typeConference Paperen_US
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