A Chinese grammatical error detection method based on a word vector with text information added

Abstract

The invention discloses a Chinese grammar error detection method and device for a word vector with text information added, belonging to the information processing field. The characteristics of the method include: firstly, vectorizing the words of the input text to form a text matrix; then, forming the text information related to each word vector by using the circulating neural network; reconstructing a text matrix; extracting the context information by using the loop neural network; then calculating the error scores of each word by using the forward neural network; using the error score to infer the wrong location. The method improves the Chinese grammar detection effect by combining the text-based word vector, and has great use values.

Description

technical field [0001] The invention relates to the field of information processing, in particular to a neural network-based Chinese grammatical error detection method. Background technique [0002] Due to the rapid development of China, more and more foreigners start to learn Chinese, so the task of Chinese grammatical error detection has attracted more and more attention. The purpose of the Chinese grammatical error detection task is to judge whether there are grammatical errors in the text written by non-Chinese native speakers, and to give an error message. [0003] Most of the current grammatical error detection models use sequence labeling. The model marks the wrong words in the text through calculation and gives an error message. Commonly used statistical learning methods for Chinese grammatical error diagnosis include n-gram, and machine learning methods include recurrent neural network methods. However, these networks require more artificial features to achieve be...

AI Technical Summary

Problems solved by technology

At present, most of the existing detection methods do not make good use of the inform

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