Artificial Intelligence Lab, Department of MIS, University of Arizona / 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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105786 |
| Date | 06 1900 |
| Creators | Chau, Michael, Xu, Jennifer J., Chen, Hsinchun |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Conference Paper |
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