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Improving Information Extraction by Discourse-Guided and Multifaceted Event Recognition

Events are one important type of information throughout text. Event extraction is an information extraction (IE) task that involves identifying entities and objects (mainly noun phrases) that represent important roles in events of a particular type. However, the extraction performance of current event extraction systems is limited because they mainly consider local context (mostly isolated sentences) when making each extraction decision. My research aims to improve both coverage and accuracy of event extraction performance by explicitly identifying event contexts before extracting individual facts. First, I introduce new event extraction architectures that incorporate discourse information across a document to seek out and validate pieces of event descriptions within the document. TIER is a multilayered event extraction architecture that performs text analysis at multiple granularities to progressively “zoom in” on relevant event information. LINKER is a unified discourse-guided approach that includes a structured sentence classifier to sequentially read a story and determine which sentences contain event information based on both the local and preceding contexts. Experimental results on two distinct event domains show that compared to previous event extraction systems, TIER can find more event information while maintaining a good extraction accuracy, and LINKER can further improve extraction accuracy. Finding documents that describe a specific type of event is also highly challenging because of the wide variety and ambiguity of event expressions. In this dissertation, I present the multifaceted event recognition approach that uses event defining characteristics (facets), in addition to event expressions, to effectively resolve the complexity of event descriptions. I also present a novel bootstrapping algorithm to automatically learn event expressions as well as facets of events, which requires minimal human supervision. Experimental results show that the multifaceted event recognition approach can effectively identify documents that describe a particular type of event and make event extraction systems more precise.