  How can we enable computers to automatically answer questions like "Who
created the character Harry Potter"? Carefully built knowledge bases provide
rich sources of facts. However, it remains a challenge to answer factoid
questions raised in natural language due to numerous expressions of one
question. In particular, we focus on the most common questions --- ones that
can be answered with a single fact in the knowledge base. We propose CFO, a
Conditional Focused neural-network-based approach to answering factoid
questions with knowledge bases. Our approach first zooms in a question to find
more probable candidate subject mentions, and infers the final answers with a
unified conditional probabilistic framework. Powered by deep recurrent neural
networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7%
on a dataset of 108k questions - the largest public one to date. It outperforms
the current state of the art by an absolute margin of 11.8%.
