A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging

A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging

Xinchi Chen, Xipeng Qiu, Xuanjing Huang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3960-3966. https://doi.org/10.24963/ijcai.2017/553

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.
Keywords:
Natural Language Processing: Tagging, chunking, syntax, and parsing
Natural Language Processing: NLP Applications and Tools
Natural Language Processing: Phonology, Morphology, and word segmentation