A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
Abstract
Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering. However, the previous neural models cannot extract the complicated feature compositions as the traditional methods with discrete features. In this work, we propose a feature-enriched neural model for joint Chinese word segmentation and part-of-speech tagging task. Specifically, to simulate the feature templates of traditional discrete feature based models, we use different filters to model the complex compositional features with convolutional and pooling layer, and then utilize long distance dependency information with recurrent layer. Experimental results on five different datasets show the effectiveness of our proposed model.
Cite
Text
Chen et al. "A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/553Markdown
[Chen et al. "A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/chen2017ijcai-feature/) doi:10.24963/IJCAI.2017/553BibTeX
@inproceedings{chen2017ijcai-feature,
title = {{A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging}},
author = {Chen, Xinchi and Qiu, Xipeng and Huang, Xuanjing},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {3960-3966},
doi = {10.24963/IJCAI.2017/553},
url = {https://mlanthology.org/ijcai/2017/chen2017ijcai-feature/}
}