Retrieving Complex Answers through Knowledge Graph and Text
Center for Language & Speech Processing(CLSP), JHU via YouTube
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Explore a lecture on advanced information retrieval techniques that combine knowledge graphs and text to provide comprehensive answers to complex queries. Delve into algorithms that automatically identify relevant entities, relations, and supporting text to generate Wikipedia-like responses for any web query. Learn about supervised retrieval models that jointly analyze web documents, Wikipedia entities, and extract passages to create knowledge articles. Discover how this approach bridges the gap between structured knowledge and unstructured text, offering users more informative and context-rich results beyond traditional "ten blue links" search. Gain insights from Laura Dietz, an expert in information retrieval and knowledge graphs, as she shares her research on improving complex answer retrieval systems.
Syllabus
Retrieving Complex Answers through Knowledge Graph and Text -- Laura Dietz
Taught by
Center for Language & Speech Processing(CLSP), JHU