Chinese Relation Extraction with Multi-Grained Information andExternal Linguistic Knowledge
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 realworld 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.