Chinese grammar debugging method and system based on multivariate text features

Abstract

The invention discloses a Chinese grammar debugging method and system based on multivariate text features. The method comprises the steps: (1) carrying out vector representation on a text through a pre-training model and grammar priori knowledge to obtain a semantic feature vector and a part-of-speech feature vector, and carrying out end-to-end splicing on the part-of-speech feature vector and thesemantic feature vector to obtain a vector sequence of the text; (2) extracting a feature vector sequence of the text by using a Bi-LSTM model; (3) carrying out attention enhancement based on semantic and part-of-speech matching information on the feature vector sequence; (4) performing linear transformation on the feature vector sequence after attention enhancement to obtain a label prediction sequence; (5) carrying out information enhancement based on word order relation characteristics on the label prediction sequence; and (6) capturing the constraint information of the label prediction sequence after information enhancement, and judging a grammar error boundary position based on the constraint information. Through verification, the method shows a good error checking effect and is superior to other existing similar methods.

Description

technical field [0001] The invention belongs to the technical field of text recognition, and in particular relates to a Chinese grammar error checking method and system based on multiple text features. Background technique [0002] In recent years, grammatical error checking methods mainly include grammatical error checking methods based on rule matching, grammatical error checking methods based on statistical models, and grammatical error checking methods based on deep learning models. [0003] The grammatical error checking method based on rule matching first extracts features such as lexical, syntactic, and grammatical features, then constructs a grammatical rule template, and finally realizes grammatical error checking by matching the text with the grammatical rule template. Grammatical rules can better describe the special grammatical structure in the text, but when the text contains complex grammatical structures and informal expressions, the grammatical rules often ca...

AI Technical Summary

Problems solved by technology

Although the above model improves the semantic representation ability of the text, it does not effectively use the prior knowledge of grammar, and the effect of grammar error checking needs to be improved

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