  Constructing a machine that understands human language is one of the most
elusive and long-standing challenges in artificial intelligence. This thesis
addresses this challenge through studies of reading comprehension with a focus
on understanding entities and their relationships. More specifically, we focus
on question answering tasks designed to measure reading comprehension. We focus
on entities and relations because they are typically used to represent the
semantics of natural language.
