MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network

Yu Lun Hsieh, Yung-Chun Chang, Yi Jie Huang, Shu Hao Yeh, Chun-Hung Chen, Wen Lian Hsu

研究成果: 書貢獻/報告類型會議貢獻

摘要

Part-of-speech (POS) tagging and named entity recognition (NER) are crucial steps in natural language processing. In addition, the difficulty of word segmentation places extra burden on those who deal with languages such as Chinese, and pipelined systems often suffer from error propagation. This work proposes an endto-end model using character-based recurrent neural network (RNN) to jointly accomplish segmentation, POS tagging and NER of a Chinese sentence. Experiments on previous word segmentation and NER competition datasets show that a single joint model using the proposed architecture is comparable to those trained specifically for each task, and outperforms freely-available softwares. Moreover, we provide a web-based interface for the public to easily access this resource.
原文英語
主出版物標題Proceedings of the Eighth International Joint Conference on Natural Language Processing
發行者Asian Federation of Natural Language Processing
頁面80-85
出版狀態已發佈 - 2017

引用此

Hsieh, Y. L., Chang, Y-C., Huang, Y. J., Yeh, S. H., Chen, C-H., & Hsu, W. L. (2017). MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network. 於 Proceedings of the Eighth International Joint Conference on Natural Language Processing (頁 80-85). Asian Federation of Natural Language Processing.