A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
This work addresses a domain-specific NLP task for Chinese language processing, representing an incremental improvement over previous neural models.
The authors tackled the problem of joint Chinese word segmentation and part-of-speech tagging by proposing a feature-enriched neural model that simulates traditional feature templates using convolutional, pooling, and recurrent layers, achieving effectiveness demonstrated on five datasets.
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.