Question Answering on Freebase via Relation Extraction and Textual Evidence

@inproceedings{Xu2016QuestionAO,
  title={Question Answering on Freebase via Relation Extraction and Textual Evidence},
  author={Kun Xu and Siva Reddy and Yansong Feng and Songfang Huang and Dongyan Zhao},
  booktitle={ACL},
  year={2016}
}
Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based… CONTINUE READING
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  • Experiments on the WebQuestions question answering dataset show that our method achieves an F1 of 53.3%, a substantial improvement over the state-of-the-art.

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