A Parallel Corpus-Based Approach to the Crime Event Extraction for Low-Resource Languages

Khairova N. Mamyrbayev O. Rizun N. Razno M. Galiya Y.
2023 Institute of Electrical and Electronics Engineers Inc.

IEEE Access
2023 #11 54093 - 54111 pp.

These days, a lot of crime-related events take place all over the world. Most of them are reported in news portals and social media. Crime-related event extraction from the published texts can allow monitoring, analysis, and comparison of police or criminal activities in different countries or regions. Existing approaches to event extraction mainly suggest processing texts in English, French, Chinese, and some other resource-rich and well-annotated languages. This paper presents a parallel corpus-based approach that follows a closed-domain event extraction methodology to event extraction from web news articles in low-resource languages. To identify the event, its arguments, and the arguments roles in the source-language part of the corpus we utilize an enhanced pattern-based method that involves the multilingual synonyms dictionary with knowledge about crime-related concepts and logic-linguistic equations. The event extraction from the target-language part of the corpus uses a cross-lingual crime-related event extraction transfer technique that is based on supplementary knowledge about the semantic similarity patterns of the considered pair of languages. The presented approach does not require a preliminarily annotated corpus for training making it more attractive to low-resource languages and allows extracting TRANSFER, CRIME, and POLICE types of events and their seven subtypes from various topics of news articles simultaneously. Implementation of our approach for the Russian-Kazakh parallel corpus of news portals articles allowed obtaining the F1-measure of crime-related event extraction of over 82% for the source language and 63% for the target language.

crime analysis , Cross-lingual transfer , event extraction , low-resource language , natural language processing , parallel corpus , semantic annotation

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National Technical University, Kharkiv Polytechnic Institute, Department of Intelligent Computer Systems, Kharkiv, 61002, Ukraine
Umeå University, Department of Computer Science, Umeå, 901 87, Sweden
Institute of Information and Computational Technologies, Almaty, 050010, Kazakhstan
Gdańsk University of Technology, Department of Informatics in Management, Gdańsk, 80-233, Poland
Friedrich Schiller University Jena, Institut für Slawistik und Kaukasusstudien, Jena, 07743, Germany
Satbayev University, Information Processing and Storage, Department of Cybersecurity, Almaty, 050013, Kazakhstan

National Technical University
Umeå University
Institute of Information and Computational Technologies
Gdańsk University of Technology
Friedrich Schiller University Jena
Satbayev University

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