| Title | Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge |
| Authors | Li, Ziran Ding, Ning Liu, Zhiyuan Zheng, Hai-Tao Shen, Ying |
| Affiliation | Tsinghua Univ, Tsinghua Shenzhen Int Grad Sch, Beijing, Peoples R China Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China Tsinghua Univ, Inst Artificial Intelligence, Beijing, Peoples R China Tsinghua Univ, State Key Lab Intelligent Technol & Syst, Beijing, Peoples R China Peking Univ, Sch Elect & Comp Engn, Shenzhen Grad Sch, Beijing, Peoples R China |
| Issue Date | 2019 |
| Publisher | 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019) |
| Abstract | Chinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy. To address the issues, we propose a multi-grained lattice framework (MG lattice) for Chinese relation extraction to take advantage of multi-grained language information and external linguistic knowledge. In this framework, (1) we incorporate word-level information into character sequence inputs so that segmentation errors can be avoided. (2) We also model multiple senses of polysemous words with the help of external linguistic knowledge, so as to alleviate polysemy ambiguity. Experiments on three real-world datasets in distinct domains show consistent and significant superiority and robustness of our model, as compared with other baselines. The source code of this paper can be obtained from https://github.com/thunlp/Chinese_NRE. |
| URI | http://hdl.handle.net/20.500.11897/552798 |
| Indexed | ISSHP CPCI-S(ISTP) |
| Appears in Collections: | 信息工程学院 |
