A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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 and age group, in principle this can be applied to any characteristic recorded. Results indicate that statistically significant relationships exist between both age and sex in certain types of crime. Both types of language model perform with similar effectiveness. 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.