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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the Eighth International Joint Conference on Natural Language Processing
PublisherAsian Federation of Natural Language Processing
Pages80-85
Publication statusPublished - 2017

Cite this

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. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (pp. 80-85). Asian Federation of Natural Language Processing.