2016/11/16 by Xinchi Chen, Xipeng Qiu, Chen, Xinchi +3
Computer Science · Engineering · #Artificial intelligence #Artificial neural network #Computation and Language (cs.CL) #Computer science #Convolutional neural network #Deep learning #Dependency (UML) #Engineering #FOS: Computer and information sciences #Feature (linguistics) #Feature engineering #Joint (building) #Layer (electronics) #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Natural language processing #Pattern recognition (psychology) #Pooling #Segmentation #Speech recognition #Topic Modeling #Word (group theory) #cs.CL
paper · pdf · doi:10.48550/arxiv.1611.05384
openalex publication_date 2016/11/16 · arxiv created 2017/07/02 · arxiv updated 2017/07/04 · openalex created_date 2017/07/14 · openalex updated_date 2026/08/06
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.