Chinese Lexical Analysis with Deep Bi-GRU-CRF Network
Published in
arXiv.org(2018)External Links:
TL
TL;DR
A deep Bi-GRU-CRF network that jointly models word segmentation, part-of-speech tagging and named entity recognition tasks and achieves a 95.5% accuracy on the test set, roughly 13% relative error reduction over the best Chinese lexical analysis tool.
Abstract
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.
References16 items
1
Deep Semantic Role Labeling: What Works and What’s Next
2
A Local Detection Approach for Named Entity Recognition and Mention Detection
3
Neural Word Segmentation Learning for Chinese
4
Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning
5