Director of Research (if dissertation) or Advisor (if thesis)
Ji, Heng
Doctoral Committee Chair(s)
Han, Jiawei
Committee Member(s)
Zhai, Chengxiang
Callison-Burch, Chris
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Event Extraction
Event Detection
Event Simulation
Few-shot Learning
Semi-supervised Learning
Language
eng
Abstract
Events are ubiquitous in our lives: from our everyday routines to yearly resolutions, from personal affairs to national emergencies, "the only thing constant is change itself''. An event is essentially a change in the state of an object, described by an event type and its arguments. Conventionally, in the natural language processing space, events are grounded in event mentions that are on the word or phrase level. This is in fact, a pigeonholed view of events from three different aspects: (1) we only look at sentences without having the global context in mind; (2) we only care about events that are defined in the ontology; (3) we overlook relationships between event mentions. In this dissertation, we aim to improve the coverage, scope, and structure aspects of event extraction and understanding, by studying how to effectively and efficiently extract events, which events to extract, and how to obtain generalized event structure knowledge. Compared to prior work, we focus on enabling systems that are open-domain and can utilize wider context. Eventually, we showcase how multiple systems can collaborate to enable event forecasting and simulation.
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