Report Linking: Information Extraction for Building Topical Knowledge Bases

natural language processing;information extraction;machine learning;knowledge graphs;Computer Science

Wolfe, TravisKoehn, Philipp ;

Johns Hopkins University

关键词: natural language processing;    information extraction;    machine learning;    knowledge graphs;    Computer Science;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/58680/root.pdf?sequence=2&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

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.