From Linking Text to Linking Crimes: Information Retrieval, But Not As You Know It

Information retrieval techniques have been used for a long time to identify links between textual items for the automatic construction of hypertexts and electronic books where sought information can be accessed by browsing. While research work in this area has been steadily decreasing in recent years, some of the techniques developed in that context are proving very valuable in a number of new application areas. In this paper we present an approach to automatic linking of textual items that is used to prioritise criminal suspects in a police investigation. A free-text description of an unsolved crime is compared to previous offence descriptions where the offender is known. By linking the descriptions, inferences about likely suspects can be made. Language Modeling is adapted to produce a Bayesian model which assigns a probability to each suspect. An empirical study showed that the linking of free text descriptions of burglaries enables prioritisation of offenders. The model presented in this paper could be easily extended to take account of additional crime and suspect linking data, such as geographical location of crimes or suspect social networks. This would enable large networks of investigative information automatically constructed from police archives to be browsed.