Chinese Open Relation Extraction for Knowledge Acquisition

@inproceedings{Tseng2014ChineseOR,
  title={Chinese Open Relation Extraction for Knowledge Acquisition},
  author={Yuen-Hsien Tseng and Lung-Hao Lee and Shu-Yen Lin and Bo-Shun Liao and Mei-Jun Liu and Hsin-Hsi Chen and Oren Etzioni and Anthony Fader},
  booktitle={EACL},
  year={2014}
}
This study presents the Chinese Open Relation Extraction (CORE) system that is able to extract entity-relation triples from Chinese free texts based on a series of NLP techniques, i.e., word segmentation, POS tagging, syntactic parsing, and extraction rules. We employ the proposed CORE techniques to extract more than 13 million entity-relations for an open domain question answering application. To our best knowledge, CORE is the first Chinese Open IE system for knowledge acquisition. 

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References

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