Textbook in PDF format

As modern society is extremely dependent on the Internet, social networks, and developing technologies, a threat to the security of both the individual and society as a whole has emerged. The openness and global nature of the Internet create opportunities both for criminals, who can use the available information for criminal purposes, and for law enforcement officials. Police officers, in turn, can deal with the preventive processing of data from the Internet in order to prevent crimes. This type of data analysis can detect illegal or criminal actions even at the stage of their formation. Crime-Related Events Identification Based on Text Analytics Approach describes the possibilities of extracting crime-related facts from semi-structured information from various Internet sources so that crime can be combatted. This book is aimed at police officers and IT professionals working in online investigations.
The rapid digitalization of information has created new opportunities for cybercrime, necessitating advanced technological solutions for crime detection and analysis. Integrating Machine Learning methods such as text classification, clustering, and Latent Semantic Analysis, this book highlights the potential of Artificial Intelligence (AI)-driven approaches to improve public safety and address the limitations of traditional natural language processing (NLP) methods.
This book is highly relevant and will likely interest professionals and researchers working at the intersection of AI, NLP, and crime investigation. Specifically, it will be useful for NLP and AI researchers, linguists, computational linguistics experts, and cybersecurity professionals.
Preface
Introduction
Major challenges of searching for crime‑related information in a text
Existing problems of knowledge extraction from semi‑structured and unstructured
Concept of information extraction from text
Relationship between the linguistic formalisms of web content texts and the real essence of the ocially significant event
Logical‑linguistic model of fact extraction from a text corpus of crime‑related information in the Kazakh‑language text corpora
The Kazakh‑Russian parallel corpus of criminal texts
Parallel corpus‑based approach to crime event extraction
Conclusion