Abstract
Semantic role labeling (SRL) is a fundamental task in Chinese language processing, but there are three major problems about the construction of SRL corpora. First, disagreements occurred in previous studies over the definition and number of semantic roles. Second, it is hard for static predicate frames to cover dynamic predicate usages. Third, it is unable to annotate the dropped semantic roles. Abstract Meaning Representation (AMR) is a new method which provides a better solution to the above problems. The researchers use 5,000 sentences in the Chinese AMR corpus to make a comparison between AMR and other SRL resources. Data analysis shows that within the framework of AMR, it is easier to annotate semantic roles based on simplified distinction between core and non-core roles. In addition, 1,045 tokens of dropped roles are annotated under this new framework. This study indicates that AMR offers a better solution for Chinese SRL and sentence meaning processing.
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Notes
- 1.
- 2.
- 3.
The current CAMR corpus contains 10,149 sentences, and has been published at https://catalog.ldc.upenn.edu/LDC2019T07.
- 4.
The difference < 0 also contains the case of core roles being dropped, and the difference > 0 also contains the case of core roles having not being annotated. But these cases are negligible because they are few in number.
- 5.
Predicates without core roles in CAMR corpus are hard to be separated from other words, so the researchers ignore them currently.
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Acknowledgements
We thank the reviewers. This work is partially supported by project 18BYY127 under the National Social Science Foundation of China, project 61472191 under the National Science Foundation of China, and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Song, L., Wen, Y., Ge, S., Li, B., Qu, W. (2020). An Easier and Efficient Framework to Annotate Semantic Roles: Evidence from the Chinese AMR Corpus. In: Hong, JF., Zhang, Y., Liu, P. (eds) Chinese Lexical Semantics. CLSW 2019. Lecture Notes in Computer Science(), vol 11831. Springer, Cham. https://doi.org/10.1007/978-3-030-38189-9_49
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