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| Chinese Named Entity Recognition Method Based on Machine Reading Comprehension |
| LIU Yiyang1,2, YU Zhengtao1,2, GAO Shengxiang1,2, GUO Junjun1,2, ZHANG Yafei1,2, NIE Bingge1,2 |
1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504
2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650504 |
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Abstract The existing named entity recognition methods mainly consider the context information in a single sentence, rather than the impact of document-level context. Aiming at this problem, a Chinese named entity recognition method based on reading comprehension is proposed, and the idea of reading comprehension is utilized to fully mine document-level context features to support entity recognition. Firstly, for each type of entity, the entity recognition task is transformed into a question and answer task, and a triple of question, text and entity answer is constructed. Then, the triple information is passed through BERT pre-training and convolutional neural network to capture document-level text context information. Finally, the entity answer prediction is realized through the binary classifier. The experiment of named entity recognition on MSRA dataset, People's Daily public dataset and self-built dataset shows the better performance of the proposed method and the better effect of reading comprehension on entity recognition.
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Received: 07 April 2020
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| Fund:Supported by National Key Research and Development Program of China(No.2018YFC0830105,2018YFC0830101,2018YFC0830100),National Natural Science Foundation of China(No.61762056,61761026, 61972186) |
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Corresponding Authors:
GAO Shengxiang, Ph.D., associate professor. Her research interests include natural language processing.
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| About author:: LIU Yiyang, master student. His research interests include natural language proce-ssing.YU Zhengtao,Ph.D., professor.His research interests include natural language processing, information retrieval and machine translation.GUO Junjun, Ph.D., lecturer. His research interests include natural language processing.ZHANG Yafei, Ph.D., lecturer.Her research interests include natural language processing and pattern recognition.NIE Bingge, master student. His research interests include natural language proce-ssing. |
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2020, Vol. 33 
