Report Linking: Information Extraction for Building Topical Knowledge Bases
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Human language artifacts represent a plentiful source of rich, unstructured information created by reporters, scientists, and analysts. In this thesis we provide approaches for adding structure: extracting and linking entities, events, and relationships from a collection of documents about a common topic. We pursue this linking at two levels of abstraction. At the document level we propose models for aligning the entities and events described in coherent and related discourses: these models are useful for deduplicating repeated claims, finding implicit arguments to events, and measuring semantic overlap between documents. Then at a higher level of abstraction, we construct knowledge graphs containing salient entities and relations linked to supporting documents: these graphs can be augmented with facts and summaries to give users a structured understanding of the information in a large collection.