Semantic role labeling (SRL) is one of fundamental tasks in Chinese language processing. At present, it has three major problems on the construction of the SRL corpus. First, the number and frame of semantic roles are not easy to define. Second, static predicate frames are hard to cover dynamic predicate usages. Third, it is unable to annotate the dropped semantic roles. The newly designed Abstract Meaning Representation (AMR) is a novel method of representing the meaning of sentences, which offers dynamic mechanisms to solve the 3 problems. To make a better comparison between AMR and other SRL resources, we choose the Chinese AMR corpus of 5,000 sentences extracted from the Chinese Proposition Bank (CPB). Data analysis shows that in AMR, it is easier to annotate the semantic roles of a predicate with the simplified distinction between core roles and non-core roles. And 1035 tokens of dropped roles are annotated under this new framework. All indicates AMR offers a better solution for Chinese SRL and sentence meaning processing. Keywords: Abstract Meaning Representation, predicate framework, semantic role, language knowledgebase
@InProceedings{SONG18.15, author = {Li Song ,Yuan Wen ,Sijia Ge ,Bin Li ,Junsheng Zhou ,Weiguang Qu and Nianwen Xue}, title = {An Easier and Efficient Framework to Annotate Semantic Roles: Evidence from the Chinese AMR Corpus}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Kiyoaki Shirai}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-24-5}, language = {english} }
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