Abstract
The discourse clause relational semantics is the semantic relation between discourse clause relevance structures. This paper proposes a method to represent the discourse clause relational semantics as a multi-dimensional feature structure. Compared with the simple classification mechanism of discourse relations, it can reveal the discourse semantic relations more deeply. Furthermore, we built Chinese discourse clause relational semantic feature corpus, and study the clause relational semantic feature recognition. We Transfer the clause relational semantic feature recognition into multiple binary classification problems, and extract relevant classification features for experiment. Experiments show that under the best classifier (SVM), the overall semantic feature recognition effect of F1 value reaches 70.14%; each classification feature contributes differently to the recognition of different clause relational semantic features, and the connectives contributes more to the recognition of all semantic features. By adding related semantic features as classification features, the interaction between different semantic features is studied. Experiments show that the influence of different semantic features is different. The addition of multiple semantic features has a more significant effect than a single semantic feature.
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Acknowledgement
This work is supported by Basic and Applied Basic Research Foundation Project of Guangdong Province (2020A1515011056), Foundation of the Guangdong 13th Five-year Plan of Philosophy and Social Sciences (GD19CYY05), General Project of National Scientific and Technical Terms Review Committee (YB2019013), Special innovation project of Guangdong Education Department (2017KTSCX064), Bidding Project of GDUFS Laboratory of Language Engineering and Computing (LEC2019ZBKT002).
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Feng, W., Huang, X., Ren, H. (2020). Analyzing Relational Semantics of Clauses in Chinese Discourse Based on Feature Structure. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_14
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DOI: https://doi.org/10.1007/978-3-030-60457-8_14
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