Abstract
The predicate and its semantic roles compose a unified entity that conveys the semantics of a given sentence. A standard pipeline of current approaches to semantic role labeling (SRL) is that for a given predicate in a sentence, we can extract features for each candidate argument and then perform the role classification through a classifier. However, this process totally ignores the integrality of the predicate and its semantic roles. To address this problem, we present a global generative model in which a novel concept called Predicate-Arguments-Coalition (PAC) is proposed to encode the relations among individual arguments. Owing to PAC, our model can effectively mine the inherent properties of predicates and obtain a globally consistent solution for SRL. We conduct experiments on the standard benchmarks: Chinese PropBank. Experimental results on a single syntactic tree show that our model outperforms the state-of-the-art methods.
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Yang, H., Zong, C. (2014). A Global Generative Model for Chinese Semantic Role Labeling. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_1
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DOI: https://doi.org/10.1007/978-3-662-45924-9_1
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