Deep learning model of semantic direction exploration based on English V+able corpus distribution and semantic roles

Abstract

In order to improve English learning efficiency, this paper constructs a deep learning model of semantic orientation exploration based on English V+able corpus distribution and semantic roles. This article combines the practical needs of English learning and establishes an ILP model with the optimization objective of minimizing spectrum resource occupation. A traffic grooming based time aware multipath RSA algorithm (HMRSA-TG) is proposed to solve the standardization problem of English speech recognition. To improve the system efficiency of intelligent English learning systems, a traffic grooming based time aware multipath RSA algorithm (HMRSA-TG) is proposed. Through research, it has been verified that the semantic orientation exploration deep learning model based on the distribution of semantic roles in English V+able corpora can effectively improve the effectiveness of English speech learning. The corpus model proposed in this paper can provide a reliable benchmark database for many speech problem learners and play an important role in English translation software in recognizing input speech with different accents