Multi-feature Chinese semantic role labeling combined with global information

Ning Ma; Jiahao Wang; Ao Zhu

doi:10.1117/12.2682493

26 May 2023 Multi-feature Chinese semantic role labeling combined with global information

Ning Ma, Jiahao Wang, Ao Zhu

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127000B (2023) https://doi.org/10.1117/12.2682493
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

Semantic role labeling serves as a crucial approach to obtaining semantic information and enabling shallow semantic analysis. Currently, the BiLSTM-CRF model is the primary method used for semantic role labeling. However, this model has many network parameters and is unable to effectively capture semantic information in long sentences. To address these issues, this paper proposes a CNN-BiLSTM-MaxPool-CRF fusion model for Chinese semantic role labeling. The model utilizes MaxPool to sample and extract the output of the BiLSTM network to optimize the network structure. Convolution kernels of differing sizes are utilized to capture local features of sentences, and these features are then combined through average pooling to form new feature vectors. These new features incorporate semantic information of the sentence context and are inputted into the model alongside multi-level linguistic feature groups such as part of speech and sentence phrase structure. Through multiple sets of experimental demonstrations, the method proposed in this paper has demonstrated significant improvements in the performance of the semantic role labeling model.

Citation Download Citation

Ning Ma, Jiahao Wang, and Ao Zhu "Multi-feature Chinese semantic role labeling combined with global information", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127000B (26 May 2023); https://doi.org/10.1117/12.2682493

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