In this thesis, we study the importance of background knowledge in relation extraction systems. We not only demonstrate the benefits of leveraging background knowledge to improve the systems' performance but also propose a principled framework that allows one to effectively incorporate knowledge into statistical machine learning models for…

Discussion of users and information retrieval systems highlights a study that investigated the representation of users' knowledge by examining their names for objects and concepts, agreement on names, the Library of Congress Subject Headings representation for similar objects and concepts, and measurement of the similarity between these…

Focuses on natural language processing (NLP) in information retrieval. Defines the seven levels at which people extract meaning from text/spoken language. Discusses the stages of information processing; how an information retrieval system works; advantages to adding full NLP to information retrieval systems; and common problems with information…