Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues

@inproceedings{Gardner2013ImprovingLA,
  title={Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues},
  author={Matthew Gardner and Partha Pratim Talukdar and Bryan Kisiel and Tom Michael Mitchell},
  booktitle={EMNLP},
  year={2013}
}
Automatically constructed Knowledge Bases (KBs) are often incomplete and there is a genuine need to improve their coverage. Path Ranking Algorithm (PRA) is a recently proposed method which aims to improve KB coverage by performing inference directly over the KB graph. For the first time, we demonstrate that addition of edges labeled with latent features mined from a large dependency parsed corpus of 500 million Web documents can significantly outperform previous PRAbased approaches on the KB… CONTINUE READING

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