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        <title>MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network</title>
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        <namePart type="given">Yu-Lun</namePart>
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        <namePart type="given">Shu-Hao</namePart>
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    <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 additional burden on those who intend to deal with languages such as Chinese, and pipelined systems often suffer from error propagation. This work proposes an end-to-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 datasets show that a single model with 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.</abstract>
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%0 Conference Proceedings
%T MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network
%A Hsieh, Yu-Lun
%A Chang, Yung-Chun
%A Huang, Yi-Jie
%A Yeh, Shu-Hao
%A Chen, Chun-Hung
%A Hsu, Wen-Lian
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 nov
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F hsieh-etal-2017-monpa
%X 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 additional burden on those who intend to deal with languages such as Chinese, and pipelined systems often suffer from error propagation. This work proposes an end-to-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 datasets show that a single model with 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.
%U https://aclanthology.org/I17-2014
%P 80-85

Markdown (Informal)

[MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network](https://aclanthology.org/I17-2014) (Hsieh et al., IJCNLP 2017)

• MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network (Hsieh et al., IJCNLP 2017)

ACL

• Yu-Lun Hsieh, Yung-Chun Chang, Yi-Jie Huang, Shu-Hao Yeh, Chun-Hung Chen, and Wen-Lian Hsu. 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 (Volume 2: Short Papers), pages 80–85, Taipei, Taiwan. Asian Federation of Natural Language Processing.