Exploration of Computer Linguistics Model Algorithms Based on Deep Learning

How to build an effective language model in NLP field to capture the statistical laws of language, improve the performance of machine translation, and enable machines to understand and generate texts with complex semantic structures is still a problem that cannot be ignored. Firstly, word segmentation, cleaning, etc., and word embedding using Word2Vec pre-training model are carried out. Then the Transformer model is constructed, and the whole sequence is processed simultaneously through the self-attention mechanism. In this paper, the learning rate, the size of hidden layer and the number of layers are adjusted by grid search, and the dropout technology is used to prevent over-fitting. Transformer model performs well in machine translation tasks, with accuracy exceeding 95%, and the METEOR value is generally higher than that of LSTM model, which is stable at a high level. In 20 different text generation tasks, the BERT score of the model is between 0.83 and 0.99, which shows that the model can understand and generate complex semantic structures. These results prove the effectiveness of Transformer model in dealing with natural language tasks, and provide new directions and ideas for future research and application.