Extraction of Event Structures from Text

 Events are a key semantic component integral to information extraction and nat?ural language understanding, which can potentially enhance many downstream applications. Despite their importance, they have received less attention in research on natural language processing. Salient properties of events are that they are a ubiquitous linguistic phenomenon appearing in various domains and that they compose rich discourse structures via event coreferences, forming a coherent story over multiple sentences. 

The central goal of this thesis is to devise a computational method that models the structural property of events in a principled framework to enable more sophisticated event detection and event coreference resolution. To achieve this goal, we address five important problems in these areas: (1) restricted domains in event detection, (2) data sparsity in event detection, (3) lack of subevent detection, (4) error propagation in pipeline models, and (5) limited applications of events. For the first two problems, we introduce a new paradigm of open-domain event detection and show that it is feasible for a distant supervision method to build models detecting events robustly in various domains while obviating the need for human annotation of events. For the third and fourth problems, we show how structured learning models are capable of capturing event interdependencies and making more informed decisions on event coreference resolution and subevent detection. Lastly, we present a novel application of event structures for question generation, illustrating usefulness of event structures as inference steps in reading comprehension by humans.