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ANALYSIS OF ACTOR INTERACTIONS IN INTERNATIONAL RELATIONSAuthor: Venu Madhav Kattagoni Date: 2018-09-07 Report no: IIIT/TH/2018/67 Advisor:Navjyoti Singh AbstractAbstract International Relations deals with the interaction of political entities in the international arena. Iden- tifying and analyzing these interactions in near real-time is important to predict upcoming political alignments and fathom upcoming conflicts. This effort is widespread as part of governmental diplo- macy. Our approach presents a computational notion of this same phenomenon. This requires analysis and data which was scarce and highly secured. Technology and computation, was limited and expen- sive. Classified documents regarding the interactions weren’t public. With increased digitization and penetration of internet across domains, this research has renewed impetus. One of the major tasks of analyzing actor interactions is to identify events involving actors from the news. Event is one of the most important temporal phenomena. It is that which has a beginning and an end. Automatic coding or classification of events happening in international relations is an important part of social science data ecosystem. The last few decades have witnessed significant work in detecting political events in the international arena. Most of the current work involves classification of news based on pattern matching from a large set of verb patterns, political actors, compound nouns, compound verb phrases, reference to pronouns and deep parsing of sentences in news articles. We introduce a methodology for extracting international relations events using news media data. This method involves a graph based unsupervised learning method for extracting topics in the news articles and identifying events based on various entities: actors and other spatial and temporal features. Since the same event might be reported by multiple news media across the world, multiple events corresponding to the same real-world event would be detected. This is referred as Event Coreference in NLP. We work on the problem of Event Coreference resolution which consists of finding clusters of event mentions that refer to the same event. Since events are complex, they co-refer fully as well as partially. We extract full co-reference, partial co-reference (sub-event and member-of relations) for the events extracted based on rule-based approach and obtain substantially good results. International relations events require an ontology for the classification of events to analyze the in- teractions among actors. We propose a novel mediation ontology for the categorization of international relations events and also introduce a novel method of classifying events through the mediation ontol- ogy using Beth Levin Verbs Classification, word2vec and Universal dependencies. The selected feature space is a result of mapping language entities to ontological entities. Hereafter, we explore various applications of international relations events. We demonstrate the relevance of our work in IREvent2Story. Event detection is a key aspect of story development which is itself composed of multiple narrative layers. Most of the narratives are template-based and follow a narration theory. We demonstrate a narrative from events detected in the international relations which presents interactions of international actors over various topics and other interactive visualizations. Another application we demonstrate is IREvent2Perspectives. Media is a classic example of the freedom to expression which may incorporate polar sentiments on the reportage. We create a system for readers with various perspectives on the same international relations event from various news sources across the world. Our study opens newer avenues for journalists and policy makers to exploit the opportunities for data driven diplomacy. It is hoped, that this research finds base especially with national and international organizations that the inexact and amorphous nature of world diplomacy is handled on hard facts and real analysis. Full thesis: pdf Centre for Exact Humanities |
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