Abstract
Deep learning and neural network have been widely used in the field of speech, vocabulary, text, pictures, and other information processing fields, which has achieved excellent research results. Neural network algorithm and prediction model were used in this paper for the study and exploration of English grammar. Aiming at the application requirements of English grammar accuracy and standardization, we proposed a machine learning model based on LSTM-CRF to detect and analyze English grammar. This paper briefly summarized the development trend of deep learning and neural network algorithm and designed the structure pattern of radial basis function neural network in grammar semantic detection and analysis on the basis of deep learning artificial neural network theory. Based on the morphological features of English grammar, a grammar database was established according to the rules of English word segmentation. In this paper, we proposed an improved conditional random field CRF (Conditional Random Field) network model based on LSTM (Long Short-Term Memory) neural network. It can improve the problem that the traditional machine learning model relies on feature point selection in English grammar detection. The machine learning model based on LSTM-CRF was used to recognize English grammar text entities. The results show that the English grammar detection system based on the LSTM-CRF model can simplify the process structure in the recognition process, reduce the unnecessary operation cycle, and improve the overall detection accuracy.
Highlights
Whether the termination conditions are metEnglish Grammar Word Segmentation Technology Based on LSTM-CRF Machine Learning Model
E English language is one of the common languages in various fields of scientific research and cooperation
In order to improve the poor training effect caused by the complexity of the input data source in the traditional algorithm, we introduce the radial basis function neural network algorithm. is algorithm is used to replace the nonlinear variable in the traditional algorithm
Summary
English Grammar Word Segmentation Technology Based on LSTM-CRF Machine Learning Model. E commonly used word segmentation methods include traditional machine learning, deep neural network learning, and so on due to the limitations of traditional word segmentation, such as the storage of vocabulary number set, incomplete grammar rules, grammar conflict, and so on At this time, we can construct word segmentation according to the CRF model. We use LSTM level and CRF level to set syntax tags, which effectively improves the problem of feature point selection and input. E model based on LSTM-CRF can use the change data of time series to obtain feature points. E feature point variables are constructed by characters, the output layer data is calculated, and the result matrix is obtained. E formula after the optimization of the calculation results by likelihood estimation is as follows: Data
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