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.