Machine Learning for Information Extraction in Informal Domains
Machine Learning - Special issue on information retrieval
Noun-phrase co-occurrence statistics for semiautomatic semantic lexicon construction
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Independence and commitment: assumptions for rapid training and execution of rule-based POS taggers
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
TEG—a hybrid approach to information extraction
Knowledge and Information Systems
Using a text engineering framework to build an extendable and portable IE-based summarisation system
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
Extracting meaningful entities from police narrative reports
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
Crime Pattern Detection Using Data Mining
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
ARE: instance splitting strategies for dependency relation-based information extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Opportunities for improving eGovernment: using language technology in workflow management
Proceedings of the 6th International Conference on Theory and Practice of Electronic Governance
Named entities in judicial transcriptions: extended conditional random fields
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Automated crime report analysis and classification for e-government and decision support
Proceedings of the 14th Annual International Conference on Digital Government Research
Crime profiling for the Arabic language using computational linguistic techniques
Information Processing and Management: an International Journal
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Much information that could help solve and prevent crimes is never gathered because the reporting methods available to citizens and law enforcement personnel are not optimal. Detectives do not have sufficient time to interview crime victims and witnesses. Moreover, many victims and witnesses are too scared or embarrassed to report incidents. We are developing an interviewing system that will help collect such information. We report here on one component, the crime information extraction module, which uses natural language processing to extract crime information from police reports, newspaper articles, and victims' and witnesses' crime narratives. We tested our approach with two types of document: police and witness narrative reports. Our algorithms extract crime-related information, namely weapons, vehicles, time, people, clothes, and locations. We achieved high precision (96%) and recall (83%) for police narrative reports and comparable precision (93%) but somewhat lower recall (77%) for witness narrative reports. The difference in recall was significant at p