¹School of Mathematics Sciences, Huaqiao University, 362021, China
²Cognitive Science Department, Xiamen University, Xiamen, 361005, China
³Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen,361005, China

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Abstract—Semantic role labeling (SRL) is a fundamental task in natural language processing to find a sentence-level semantic representation. At present, the mainstream studies of semantic role labeling focus on the use of a variety of statistical machine learning techniques. But it difficult to obtain high quality labeled data. To solve the problem, we proposed a novel prototype patterns selection algorithm based on semi-supervised learning in this paper. There are two main innovations in this article: firstly, order parameter evolution is introduced to expand training data. The strongest order parameter will win by competition and desired pattern will be selected. Secondly, the must-links and cannot-links constraints exist in the train data is used to reduce the noise of extend data. The experiment results show the proposed method has a higher performance for semantic role labeling.

Index Terms—Semi-supervised learning, SRL, SNN, Order parameters.

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Cite: Zhehuang Huang, Yidong Chen, "A Prototype Patterns Selection Algorithm Based on Semi-supervised Learning," Journal of Software vol. 8, no. 8, pp. 1984-1990, 2013.