CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
Zihang Dai, Lei Li, Wei Hong Xu · 2016
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 neuralnetwork-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%.