Abstract
With the development of big data and deep learning, Chinese information processing systems need more comprehensive quantitative research to carry out large-scale automatic analysis. Syntactic and semantic analysis is a hot topic in the field in recent years. Existing research on Chinese verbs of confession lacks syntactic and semantic exploration based on large-scale corpora. This study has extracted and selected sentences with verbs of confession from several Chinese corpora. This study then has used a self-developed syntactic and semantic annotation tool to check the automatic annotation results based on dependency grammar and further explored the features of these verbs. It was found that the five verbs all appear in four syntactic dependencies: HED (Head), VOB (Verb-Object), ATT (Attribute) and SBV (Subject-Verb); their most frequently collocated semantic roles are Agent, Content and Time. This study is of value for enriching the deep knowledge of Chinese verbs and providing references for lexicography, language teaching and natural language processing.
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Notes
- 1.
Translations of these terms are on this website: https://ltp.ai/docs/appendix.html#id5. But the developer confirmed that some translations have errors on April 26, 2022, which confuse dependency relations with the role of the dependents themselves. Since no new official translations have been provided yet, this paper still uses the current translations on the website, except changing the verb “coordinate” into the noun “coordination”.
- 2.
Relative of the Knowledge-Base is the same as Content in this study.
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Acknowledgement
The study is supported by the University of Macau (MYRG2019-00013-FAH). This work was performed in part at the high performance computing cluster (HPCC) which is supported by information and communication technology office (ICTO) of the University of Macau.
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Wang, S. (2022). Investigating Verbs of Confession Through a Syntactic and Semantic Annotation Tool. In: Dong, M., Gu, Y., Hong, JF. (eds) Chinese Lexical Semantics. CLSW 2021. Lecture Notes in Computer Science(), vol 13249. Springer, Cham. https://doi.org/10.1007/978-3-031-06703-7_15
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