Information Extraction Models for Crime Domain: A Review

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Muayad N. Abdullah

Abstract

Information Extraction refers to the systematic technique of obtaining valuable information from ‎unstructured ‎texts. Named Entity Recognition is a part of information extraction, and is the ‎process of extracting named entities of interest from text, such as person names, places, events, ‎and entities, among others. Named Entity models are useful in critical situations where time and ‎accuracy are important, such as when crime analysts need particular information about a crime case ‎to help them solve it. In the crime domain, police and crime analysts need immediate information on certain crime cases to solve the crime or prevent it from happening again. Crime news documents consist of details on crimes and these details make these documents beneficial. crime analysts are able to quickly and accurately extract beneficial ‎information from unstructured text. With the increase in crime rates all over the world because of ‎the increase in the population, many Named Entity models have been proposed to help crime ‎analysts solve crime cases.   Many studies have been conducted on the performance of these methods based on general News wire articles. This work reviews several Named Entity models used for the crime ‎domain.

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How to Cite
[1]
“Information Extraction Models for Crime Domain: A Review”, JUBPAS, vol. 32, no. 1, pp. 205–223, Mar. 2024, doi: 10.29196/chpjsp35.
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Articles

How to Cite

[1]
“Information Extraction Models for Crime Domain: A Review”, JUBPAS, vol. 32, no. 1, pp. 205–223, Mar. 2024, doi: 10.29196/chpjsp35.

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