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Abstract Although the traditional conditional random fields (CRF) method can accommodate any length of context information and the feature design is flexible, but the training cost is high and the model complexity is high, especially in the sequence tagging task,because the joint probability distribution of the whole tagging sequence needs to be calculated, its shortcomings are more exposed.For this reason, this paper combines a structured support vector machine (SSVM) method to do part of speech tagging research according to Agglutinating Language word formation features and context information of corpus. Compared with traditional SVM, this model can fit the distribution of feature functions by adding additional constraints, and then be used to deal with tagging in different fields. In this paper, the Agglutinating Language part of speech tagging experiment results show that the accuracy of SSVM is higher compared with the traditional part of speech tagging algorithm.
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Published: 10 January 2021
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