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Word-based method for Chinese Part-of-Speech via Parallel and Adversarial Network

Chinese Journal of Electronics 2022年第2期31卷 337-344页

作者： HUANG Kaiyu CAO Jingxiang LIU Zhuang HUANG Degen School of Computer Science and Technology Dalian University of Technology School of Foreign Languages Dalian University of Technology School of Applied Finance Dongbei University of Finance and Economics

Chinese part-of-speech(POS) tagging is an essential task for Chinese downstream natural language processing tasks. The accuracy of the Chinese POS task will drop dramatically by word-based methods because of the segmentation errors and the word ***, there are several Chinese POS tagging sets with different criteria. Some of them only have a small-scale annotated corpus and are hard to train. To this end, we propose a modified word-based transformer neural network architecture. Meanwhile, we utilize an adversarial transfer learning method that splits the architecture into shared and private parts. This work directly improves the ability of the word-based model, instead of adopting a joint character-based method. Extensive experiments show that our method achieves state-of-the-art performance on all datasets, and more importantly, our method improves performance effectively for the word-based Chinese sequence labeling task.

关键词： Chinese downstream natural language processing tasks modified word-based transformer neural network architecture word-based Chinese sequence labeling task word sparsity adversarial transfer learning method Chinese part-of-speech tagging neural nets word processing word-based method adversarial network Chinese POS task text analysis Chinese POS tagging sets word-based model learning (artificial intelligence) joint character-based method natural language processing small-scale annotated corpus

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