Chinese Lexical Analysis with Deep Bi-GRU-CRF Network
This work addresses lexical analysis for Chinese language processing, representing an incremental improvement over existing methods.
The paper tackled Chinese lexical analysis by developing a deep Bi-GRU-CRF network that jointly models word segmentation, part-of-speech tagging, and named entity recognition, achieving 95.5% accuracy on the test set, which is a roughly 13% relative error reduction over their previous best tool.
Lexical analysis is believed to be a crucial step towards natural language understanding and has been widely studied. Recent years, end-to-end lexical analysis models with recurrent neural networks have gained increasing attention. In this report, we introduce a deep Bi-GRU-CRF network that jointly models word segmentation, part-of-speech tagging and named entity recognition tasks. We trained the model using several massive corpus pre-tagged by our best Chinese lexical analysis tool, together with a small, yet high-quality human annotated corpus. We conducted balanced sampling between different corpora to guarantee the influence of human annotations, and fine-tune the CRF decoding layer regularly during the training progress. As evaluated by linguistic experts, the model achieved a 95.5% accuracy on the test set, roughly 13% relative error reduction over our (previously) best Chinese lexical analysis tool. The model is computationally efficient, achieving the speed of 2.3K characters per second with one thread.