Abstract
Word segmentation, part-of-speech (POS) tagging, and syntactic parsing are three fundamental Chinese analysis tasks for Chinese language processing, which are also crucial for various downstream tasks such as machine translation and information extraction. To achieve high accuracy for these tasks, treebanks that contain sentences manually annotated with word segmentation, part-of-speech tags, and phrase structures are essential. Although there are large-scale Chinese treebanks in the news domain, such treebanks are unavailable in the scientific domain. This significantly limits the performance of Chinese language processing for scientific text. To address this problem, we annotate the 2nd version of the Chinese treebank in the scientific domain (SCTB-V2). SCTB-V2 contains 12,175 sentences annotated with word segmentation, part-of-speech tags, and phrase structures. We conducted Chinese analyses and machine translation experiments on SCTB-V2. The results show the effectiveness of SCTB-V2. We release this treebank to promote scientific Chinese language processing research http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?A%20Chinese%20Treebank%20 in%20Scientific%20Domain%20%28SCTB%29.
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Notes
Statistics from Japan Patent Office.
As both of the two workers were well trained through the entire two years annotation period, we did not observe biases introduced by the baseline systems in the final SCTB-V2.
Preliminary experiments show that the union is better than using one of them only.
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Acknowledgements
This work was supported by “Project on Practical Implementation of Japanese to Chinese-Chinese to Japanese Machine Translation,” JST. We sincerely thank Ms. Fumio Hirao and Mr. Teruyasu Ueki, who annotated SCTB-V2. We are appreciated Mr. Frederic Bergeron for his development of the SynTree toolkit to speed up the annotation process. Finally, we want to thank Dr. Mo Shen for valuable discussions regarding annotation standards.
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Chu, C., Mao, Z., Nakazawa, T. et al. SCTB-V2: the 2nd version of the Chinese treebank in the scientific domain. Lang Resources & Evaluation 57, 1389–1403 (2023). https://doi.org/10.1007/s10579-022-09615-2
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DOI: https://doi.org/10.1007/s10579-022-09615-2