Journal article
An associate constraint network approach to extract multi-lingual information for crime analysis
Decision Support Systems, v 43(4), pp 1348-1361
2007
Abstract
International crime and terrorism have drawn increasing attention in recent years. Retrieving relevant information from criminal records and suspect communications is important in combating international crime and terrorism. However, most of this information is written in languages other than English and is stored in various locations. Information sharing between countries therefore presents the challenge of cross-lingual semantic interoperability. In this work, we propose a new approach – the associate constraint network – to generate a cross-lingual concept space from a parallel corpus, and benchmark it with a previously developed technique, the Hopfield network. The associate constraint network is a constraint programming based algorithm, and the problem of generating the cross-lingual concept space is formulated as a constraint satisfaction problem. Nodes and arcs in an associate constraint network represent extracted terms from parallel corpora and their associations. Constraints are defined for the nodes in the associate constraint network, and node consistency and network satisfaction are also defined. Backmarking is developed to search for a feasible solution. Our experimental results show that the associate constraint network outperforms the Hopfield network in precision, recall and efficiency. The cross-lingual concept space that is generated with this method can assist crime analysts to determine the relevance of criminals, crimes, locations and activities in multiple languages, which is information that is not available in traditional thesauri and dictionaries.
Metrics
Details
- Title
- An associate constraint network approach to extract multi-lingual information for crime analysis
- Creators
- Christopher C Yang - Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong KongKar Wing Li - Department of Information Systems, City University of Hong Kong, Hong Kong
- Publication Details
- Decision Support Systems, v 43(4), pp 1348-1361
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000249482800019
- Scopus ID
- 2-s2.0-34547666975
- Other Identifier
- 991014877858204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Operations Research & Management Science