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An SNN-Based Semantic Role Labeling Model with Its Network Parameters Optimized Using an Improved PSO Algorithm

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Abstract

Semantic role labeling (SRL) is a fundamental task in natural language processing to find a sentence-level semantic representation. The semantic role labeling procedure can be viewed as a process of competition between many order parameters, in which the strongest order parameter will win by competition and the desired pattern will be recognized. To realize the above-mentioned integrative SRL, we use synergetic neural network (SNN). Since the network parameters of SNN directly influence the synergetic recognition performance, it is important to optimize the parameters. In this paper, we propose an improved particle swarm optimization (PSO) algorithm based on log-linear model and use it to effectively determine the network parameters. Our contributions are two-folds: firstly, a log-linear model is introduced to PSO algorithm which can effectively make use of the advantages of a variety of different knowledge sources, and enhance the decision making ability of the model. Secondly, we propose an improved SNN model based on the improved PSO and show its effectiveness in the SRL task. The experimental results show that the proposed model has a higher performance for semantic role labeling with more powerful global exploration ability and faster convergence speed, and indicate that the proposed model has a promising future for other natural language processing tasks.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (grant numbers 61005052, 61303082), the Key Technologies R&D Program of China (Grant number 2012BAH14F03), the Fundamental Research Funds for the Central Universities (Grant number 2010121068), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant number 20130121110040) and the Natural Science Foundation of Fujian Province of China (Grant number 2011J01369).

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Authors and Affiliations

  1. School of Information Science and Engineering, Xiamen University, Xiamen, 361005, Fujian, China

    Yidong Chen, Zhehuang Huang & Xiaodong Shi

  2. School of Mathematics Sciences, Huaqiao University, Quanzhou, 362021, Fujian, China

    Zhehuang Huang

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  1. Yidong Chen
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  2. Zhehuang Huang
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  3. Xiaodong Shi
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Correspondence to Yidong Chen.

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Chen, Y., Huang, Z. & Shi, X. An SNN-Based Semantic Role Labeling Model with Its Network Parameters Optimized Using an Improved PSO Algorithm. Neural Process Lett 44, 245–263 (2016). https://doi.org/10.1007/s11063-015-9449-y

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