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.
|Title of host publication||Proceedings of the Eighth International Joint Conference on Natural Language Processing|
|Publisher||Asian Federation of Natural Language Processing|
|Publication status||Published - 2017|
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.