1School of Information Management, Nanjing University, Nanjing 210023, China 2College of Information Management, Nanjing Agricultural University, Nanjing 210095, China 3College of Literature, Nanjing Normal University, Nanjing 210097, China
[Objective] This paper constructs a deep learning model for automatic word segmentation and part-of-speech (POS) tagging of ancient literature, aiming to build an automatic annotation solution for Chinese books from multiple fields. [Methods] We used 25 Pre-Qin literature as training corpus, which covers the Confucian classics, history, philosophy and miscellaneous works. Then, we constructed a unified model with BERT for word segmentation and POS tagging without adding new features. Third, we examined our model with The Records of the Grand Historian, which was not included in the training corpus. Finally, we analyzed the four basic parts constituting historical events (names, locations, time, actions) with statistics and case studies. [Results] The proposed model’s F-score for word segmentation and the POS-tagging reached 95.98% and 88.97%. [Limitations] After analyzing the confusion heat map of POS tagging, it is found that the mislabeling, which is caused by the imbalanced part-of-speech distribution, the similar syntactic features of some parts of speech instances, and the multi-category words, needs further research and resolutions. [Conclusions] Our deep learning model is stable and applicable for word segmentation and POS tagging with Pre-Qin literature.
张琪,江川,纪有书,冯敏萱,李斌,许超,刘浏. 面向多领域先秦典籍的分词词性一体化自动标注模型构建*[J]. 数据分析与知识发现, 2021, 5(3): 2-11.
Zhang Qi,Jiang Chuan,Ji Youshu,Feng Minxuan,Li Bin,Xu Chao,Liu Liu. Unified Model for Word Segmentation and POS Tagging of Multi-Domain Pre-Qin Literature. Data Analysis and Knowledge Discovery, 2021, 5(3): 2-11.
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