Question Answering on Freebase via Relation Extraction and Textual Evidence

  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},
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
Highly Influential
This paper has highly influenced 13 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 83 citations. REVIEW CITATIONS

From This Paper

Figures, tables, results, and topics from this paper.

Key Quantitative Results

  • 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.


Publications citing this paper.
Showing 1-10 of 61 extracted citations

83 Citations

Citations per Year
Semantic Scholar estimates that this publication has 83 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 56 references

Similar Papers

Loading similar papers…