Abstract
The ability to infer the characteristics of offenders from their criminal behaviour ('offender profiling') has only been partially successful since it has relied on subjective judgments based on limited data. Words and structured data used in crime descriptions recorded by the police relate to behavioural features. Thus Language Modelling was applied to an existing police archive to link behavioural features with significant characteristics of offenders. Both multinomial and multiple Bernoulli models were used. Although categories selected are gender, age group, ethnic appearance and broad occupation (employed or not), in principle this can be applied to any characteristic recorded. Results indicate that statistically significant relationships exist between all characteristics for many types of crime. Bernoulli models tend to perform better than multinomial ones. It is also possible to identify automatically specific terms which when taken together give insight into the style of offending related to a particular group.
| Original language | English |
|---|---|
| Pages (from-to) | 303-315 |
| Number of pages | 13 |
| Journal | Data and Knowledge Engineering |
| Volume | 69 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Mar 2010 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 16 Peace, Justice and Strong Institutions
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