Abstract: Political and social scholars increasingly rely on event coders, which are automated systems that extract structured event representations from news articles, in order to monitor, analyze and predict conflicts and affairs involving political entities across the globe.
However, the existing event coders rest on outdated pattern matching techniques, relying on large manually maintained dictionaries composed of lexico-syntactic patterns designed for capturing conflict events. Apart from the high costs, time and specialized knowledge required to update and expand such dictionaries, these techniques do not support event extraction on multilingual corpus. As a consequence, the application of existing systems often yields low-recall results and imposes limitations when working with sources coming from different countries and languages.
In this talk, we propose deep-learning based frameworks to obtain state-of-the-art results for extracting structured events from natural language text in political and social sciences domains. We do so by exploring four main directions: (i) automatically extending the external dictionaries and knowledge bases utilized in the current event coders through knowledge extraction techniques; (ii) formulating the event coding task as a classification problem and proposing a supervised deep learning model to solve it; (iii) developing an innovative deep neural network design by combining state-of-the-art language representation models with multi-task learning technique to efficiently extract events in a structured format from multilingual corpus; and (iv) generating models to predict the state-based conflicts in Africa, including the fatalities associated with these conflicts to provide international communities with early warning signals of the humanitarian crisis.
*This work is funded by NSF, and NSA. The work is in collaboration with Dr. Patrick Brandt and Dr. Jennifer Holmes, School of Economic, Political and Policy Sciences, UT Dallas.
Bio: Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received his Ph.D. degree in Computer Science from the University of Southern California (USC) in August of 2000. Dr. Khan is a fellow of IEEE, IET, BCS, and an ACM Distinguished Scientist. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics, IEEE Big Data Security Award and IBM Faculty Award (research) 2016. Dr. Latifur Khan has published over 300 papers in premier journals and prestigious conferences. Currently, Dr. Khan’s research area focuses on big data management and analytics, data mining and its application over cyber security, complex data management including geo-spatial data and multimedia data. His research has been supported by grants from NSF, NIH, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM and HPE. More details can be found at: www.utdallas.edu/~lkhan/