Research on Influence of Text Representation of Vertigo Interrogation Texts on Classification Results

To understand the influence of the text representation of vertigo interrogation texts on classification results, our paper proposes a fusion strategy of text representation combination and constructs a new stacking model fusion. Vertigo interrogation texts of the four typical vertigo diseases are used to make classification under the different text representation. Results show that the classification effect of proposed stacking model fusion with proposed text representation combinations is superior to that of the gradient boosting decision tree, support vector machine, and naïve Bayes with the simple text representation. Results also show that the proposed text representation combination of word2vec, GloVe and one-hot is the best overall classification effect among other text representation combinations based on our proposed stacking model fusion. Therefore, we conclude that the proposed fusion strategy of text representation combination and stacking model fusion can improve the classification effect of text representation of vertigo interrogation texts.