A New Method of Metaphor Recognition for A-is-B Model in Chinese Sentences

Abstract

Metaphor recognition is the bottleneck of natural language processing, and the metaphor recognition for A-is-B mode is the difficulty of metaphor recognition. Compared with phrase recognition, the metaphor recognition for A-is-B mode is more flexible and difficult. To solve this difficult problem, the paper proposes a feature-based recognition method. First, the metaphor recognition problem for A-is-B model is transformed into a classification problem, then four sets of features of upper and lower position, sentence model, class, and Word2Vec are calculated respectively, and feature sets are constructed by using these four sets of features. The experiment uses the SVM model classifier and the neural network classifier to realize the metaphor recognition for the A-is-B mode. The experimental results show that the method using neural network classifier method has better accuracy and recall rate, 96.7% and 93.1%, respectively, but it takes more time to predict a sentence. According to the analysis of the experimental results of the two classifiers, the improved method achieved good results.