Language sense classification model based on neural network

Abstract

The common international language, English, is playing an increasingly important role in various fields with the rapid development of artificial intelligence in recent years. Artificial intelligence can improve students' English abilities as an additional teaching tool. Therefore, this study seeks the English language sense between different types of sentences based on Long Short-Term Memory (LSTM) and BERT model analysis sentences and generates a model to distinguish the types. This paper adapts the LSTM model and BERT model: first, this paper crawls the sentences from British Broadcasting Corporation (BBC) documentaries, podcasts, and YouTube and then constructs a data filter to remove the sentences with low quality and short. This paper analyzes the data set through the BERT module and LSTM model. this paper then compares the differences between different sentences in a large-scale corpus to generate a language model without long-term dependence. A model is expected to be generated after corpus analysis, and the model can be used to analyze new input statements and give their types. This study can help English learners improve their sense of the English language and the types of sentences they need to say in the face of different situations.