An Empirical Investigation of Error Types in Vietnamese Parsing

Quy Nguyen, Yusuke Miyao, Hiroshi Noji, Nhung Nguyen


Abstract
Syntactic parsing plays a crucial role in improving the quality of natural language processing tasks. Although there have been several research projects on syntactic parsing in Vietnamese, the parsing quality has been far inferior than those reported in major languages, such as English and Chinese. In this work, we evaluated representative constituency parsing models on a Vietnamese Treebank to look for the most suitable parsing method for Vietnamese. We then combined the advantages of automatic and manual analysis to investigate errors produced by the experimented parsers and find the reasons for them. Our analysis focused on three possible sources of parsing errors, namely limited training data, part-of-speech (POS) tagging errors, and ambiguous constructions. As a result, we found that the last two sources, which frequently appear in Vietnamese text, significantly attributed to the poor performance of Vietnamese parsing.
Anthology ID:
C18-1260
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3075–3089
Language:
URL:
https://aclanthology.org/C18-1260
DOI:
Bibkey:
Cite (ACL):
Quy Nguyen, Yusuke Miyao, Hiroshi Noji, and Nhung Nguyen. 2018. An Empirical Investigation of Error Types in Vietnamese Parsing. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3075–3089, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
An Empirical Investigation of Error Types in Vietnamese Parsing (Nguyen et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-1260.pdf