Integration of Automatic Sentence Segmentation and Lexical Analysis of Ancient Chinese based on BiLSTM-CRF Model

Ning Cheng, Bin Li, Liming Xiao, Changwei Xu, Sijia Ge, Xingyue Hao, Minxuan Feng


Abstract
The basic tasks of ancient Chinese information processing include automatic sentence segmentation, word segmentation, part-of-speech tagging and named entity recognition. Tasks such as lexical analysis need to be based on sentence segmentation because of the reason that a plenty of ancient books are not punctuated. However, step-by-step processing is prone to cause multi-level diffusion of errors. This paper designs and implements an integrated annotation system of sentence segmentation and lexical analysis. The BiLSTM-CRF neural network model is used to verify the generalization ability and the effect of sentence segmentation and lexical analysis on different label levels on four cross-age test sets. Research shows that the integration method adopted in ancient Chinese improves the F1-score of sentence segmentation, word segmentation and part of speech tagging. Based on the experimental results of each test set, the F1-score of sentence segmentation reached 78.95, with an average increase of 3.5%; the F1-score of word segmentation reached 85.73%, with an average increase of 0.18%; and the F1-score of part-of-speech tagging reached 72.65, with an average increase of 0.35%.
Anthology ID:
2020.lt4hala-1.8
Volume:
Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
LREC | LT4HALA | WS
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
52–58
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lt4hala-1.8
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lt4hala-1.8.pdf