A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Augmenting Naive Bayes Classifiers with Statistical Language Models
Information Retrieval
Application of Language Models to Suspect Prioritisation and Suspect Likelihood in Serial Crimes
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
Combining naive bayes and n-gram language models for text classification
ECIR'03 Proceedings of the 25th European conference on IR research
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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.