Metaphor Recognition of English Learners Based on Machine Learning Algorithm

• Main Author: Fu, X. (Author)
• Format: Article
• Language: English
• Published: Hindawi Limited 2022
• Subjects: Context-word; Joint probability distributions; Knowledge based systems; Language features; Learning algorithms; Literals; Machine learning; Machine learning algorithms; Machine learning module; Maximum entropy methods; Maximum-entropy; Naive bayes; On-machines; Probability distributions; Semantic category; Semantics; Speech recognition
• Online Access: View Fulltext in Publisher

 The advantage of machine learning algorithm lies in predicting the semantic category of new language features according to the joint probability distribution of existing language features and their semantic categories. Based on a machine learning algorithm, this paper studies the metaphor recognition of English learners. The process of metaphor recognition is described as the classification of metaphorical meaning and literal meaning. Metaphor modeling is carried out by maximum entropy and naive Bayes. Check whether the sentences in the verification set contain the language rules in the knowledge base, and semantically identify the words in the sentences according to the annotations of the knowledge base. For sentences that do not meet the language rules of the knowledge base, the machine learning module is used for further verification and identification. Based on the comprehensive features of context words and parts of speech, the ideal window of maximum entropy recognition is determined, and then the left and right position features are introduced to improve the experimental effect. In the comparative experiments of the three models, this model has obvious advantages in English learners' metaphor recognition. Its recognition accuracy is high, and it has certain practical value. © 2022 Xiaoling Fu.