We study question answering as a machine learning problem, and induce a function that
maps open-domain questions to queries over a database of web extractions. Given a large,
community-authored, question-paraphrase corpus, we demonstrate that it is possible to
learn a semantic lexicon and linear ranking function without manually annotating questions.
Our approach automatically generalizes a seed lexicon and includes a scalable,
parallelized perceptron parameter estimation scheme. Experiments show that our approach …

For the past fifteen years, search engines like Google have been the dominant way of
finding information online. However, search engines break down when presented with
complex information needs expressed as natural language questions. Further, as more
people access the web from mobile devices with limited input/output capabilities, the need
for software that can interpret and answer questions becomes more pressing. This
dissertation studies the design of Open Question Answering (Open QA) systems that answer …