ABSTRACT
The identification of criminals' behavioral patterns can be helpful for solving crimes. Currently, in order to perform this task, police investigators manually extract criminals' behavioral patterns (also referred to as criminals' modus operandi) from a large corpus of police reports. These patterns are compared to the patterns observed in an ongoing criminal investigation to identify similarities that may link the suspect to other documented crimes. Due to the large number of historical cases, this manual process is time consuming, very costly in terms of police resources, and limits the investigators' ability to solve open cases. In this study, we propose an automatic and language independent method for extracting behavioral patterns from police reports. Relying on the extracted behavioral patterns as input, we utilize a Siamese neural network to identify burglaries committed by the same criminals. Experiments performed using a large dataset of police reports written in Hebrew provided by the Israel Police demonstrate the proposed method's high performance, achieving an AUC above 0.9. Using our method, we are also able to identify potential suspects for 22.41% of the open burglary cases in Israel.
Supplemental Material
- Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2016. A simple but tough-to-beat baseline for sentence embeddings. (2016).Google Scholar
- Luca Bertinetto, Jack Valmadre, Joao F Henriques, Andrea Vedaldi, and Philip HS Torr. 2016. Fully-convolutional siamese networks for object tracking. In European conference on computer vision. Springer, 850--865.Google ScholarCross Ref
- David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research, Vol. 3, Jan (2003), 993--1022.Google ScholarDigital Library
- Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, Vol. 5 (2017), 135--146.Google ScholarCross Ref
- Anton Borg and Martin Boldt. 2016. Clustering residential burglaries using modus operandi and spatiotemporal information. International Journal of Information Technology & Decision Making, Vol. 15, 01 (2016), 23--42.Google ScholarCross Ref
- Anton Borg, Martin Boldt, Niklas Lavesson, Ulf Melander, and Veselka Boeva. 2014. Detecting serial residential burglaries using clustering. Expert Systems with Applications, Vol. 41, 11 (2014), 5252--5266.Google ScholarCross Ref
- Gerlof Bouma. 2009. Normalized (pointwise) mutual information in collocation extraction. Proceedings of GSCL (2009), 31--40.Google Scholar
- Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard S"ackinger, and Roopak Shah. 1994. Signature verification using a "siamese" time delay neural network. In Advances in neural information processing systems. 737--744.Google Scholar
- Anna L Buczak and Christopher M Gifford. 2010. Fuzzy association rule mining for community crime pattern discovery. In ACM SIGKDD Workshop on Intelligence and Security Informatics. ACM, 2.Google ScholarDigital Library
- Spencer Chainey, Lisa Tompson, and Sebastian Uhlig. 2008. The utility of hotspot mapping for predicting spatial patterns of crime. Security journal, Vol. 21, 1--2 (2008), 4--28.Google ScholarCross Ref
- Subhayu Chakravorty, Souparno Daripa, Urmi Saha, Subhasree Bose, Saptarsi Goswami, and Shinjan Mitra. 2015. Data mining techniques for analyzing murder related structured and unstructured data. American Journal of Advanced Computing, Vol. 2, 2 (2015), 47--54.Google Scholar
- François Chollet et almbox. 2015. Keras.Google Scholar
- Arpita Das, Harish Yenala, Manoj Chinnakotla, and Manish Shrivastava. 2016. Together we stand: Siamese networks for similar question retrieval. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 378--387.Google ScholarCross Ref
- H David and A Suruliandi. 2017. SURVEY ON CRIME ANALYSIS AND PREDICTION USING DATA MINING TECHNIQUES. ICTACT Journal on Soft Computing, Vol. 7, 3 (2017).Google Scholar
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).Google Scholar
- Qing Guo, Wei Feng, Ce Zhou, Rui Huang, Liang Wan, and Song Wang. 2017. Learning dynamic siamese network for visual object tracking. In Proceedings of the IEEE International Conference on Computer Vision. 1763--1771.Google ScholarCross Ref
- Alon Itai and Shuly Wintner. 2008. Language resources for Hebrew. Language Resources and Evaluation, Vol. 42, 1 (2008), 75--98.Google ScholarCross Ref
- Wolfgang Jentner, Geoffrey Ellis, Florian Stoffel, Dominik Sacha, and Daniel Keim. 2016. A visual analytics approach for crime signature generation and exploration. In IEEE VIS2016 Workshop on Temporal & Sequential Event Analysis.Google Scholar
- Dror Kamir, Naama Soreq, and Yoni Neeman. 2002. A comprehensive NLP system for modern standard Arabic and modern Hebrew. In Proceedings of the ACL-02 workshop on Computational approaches to semitic languages. Association for Computational Linguistics, 1--9.Google ScholarDigital Library
- Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. 2015. Siamese neural networks for one-shot image recognition. In ICML deep learning workshop, Vol. 2.Google Scholar
- Da Kuang, P Jeffrey Brantingham, and Andrea L Bertozzi. 2017. Crime topic modeling. Crime Science, Vol. 6, 1 (2017), 12.Google ScholarCross Ref
- Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In International conference on machine learning. 1188--1196.Google ScholarDigital Library
- Yu-Sheng Li and Ming-Liang Qi. 2019. An approach for understanding offender modus operandi to detect serial robbery crimes. Journal of Computational Science, Vol. 36 (2019), 101024.Google ScholarCross Ref
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.Google Scholar
- Amir More, Amit Seker, Victoria Basmova, and Reut Tsarfaty. 2019. Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for MRLs and a Case Study from Modern Hebrew. Transactions of the Association for Computational Linguistics, Vol. 7 (2019), 33--48.Google ScholarCross Ref
- Malith Munasinghe, Harsha Perera, Shanika Udeshini, and Ruvan Weerasinghe. 2015. Machine Learning based criminal short listing using Modus Operandi features. In Advances in ICT for Emerging Regions (ICTer), 2015 Fifteenth International Conference on. IEEE, 69--76.Google ScholarCross Ref
- Shyam Varan Nath. 2006. Crime pattern detection using data mining. In Web intelligence and intelligent agent technology workshops, 2006. wi-iat 2006 workshops. 2006 ieee/wic/acm international conference on. IEEE, 41--44.Google ScholarDigital Library
- Michael D Porter. 2016. A statistical approach to crime linkage. The American Statistician, Vol. 70, 2 (2016), 152--165.Google ScholarCross Ref
- Radim Rehurek and Petr Sojka. 2010. Software framework for topic modelling with large corpora. In In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Citeseer.Google Scholar
- Martin B Short, Maria R D'orsogna, Virginia B Pasour, George E Tita, Paul J Brantingham, Andrea L Bertozzi, and Lincoln B Chayes. 2008. A statistical model of criminal behavior. Mathematical Models and Methods in Applied Sciences, Vol. 18, supp01 (2008), 1249--1267.Google ScholarCross Ref
- Reut Tsarfaty, Amit Seker, Shoval Sadde, and Stav Klein. 2019. What's Wrong with Hebrew NLP? And How to Make it Right. arXiv preprint arXiv:1908.05453 (2019).Google Scholar
- Tong Wang et almbox. 2016. Finding patterns in features and observations: new machine learning models with applications in computational criminology, marketing, and medicine. Ph.D. Dissertation. Massachusetts Institute of Technology.Google Scholar
- Tong Wang, Cynthia Rudin, Daniel Wagner, and Rich Sevieri. 2013. Detecting Patterns of Crime with Series Finder. In AAAI (Late-Breaking Developments).Google Scholar
- Jessica Woodhams, Ray Bull, and Clive R Hollin. 2007. Case linkage. In Criminal Profiling. Springer, 117--133.Google Scholar
- Cheng Zhang, Wu Liu, Huadong Ma, and Huiyuan Fu. 2016. Siamese neural network based gait recognition for human identification. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2832--2836.Google ScholarDigital Library
- Shixiang Zhu and Yao Xie. 2019. Crime Linkage Detection by Spatial-Temporal-Textual Point Processes. arXiv preprint arXiv:1902.00440 (2019).Google Scholar
- Imed Zitouni. 2014. Natural language processing of semitic languages. Springer.Google Scholar
Index Terms
- Crime Linkage Based on Textual Hebrew Police Reports Utilizing Behavioral Patterns
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