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Research on Grammar Checking System Using Computer Big Data and Convolutional Neural Network Constructing Classification Model
- Source :
- Journal of Physics: Conference Series. 1952:042097
- Publication Year :
- 2021
- Publisher :
- IOP Publishing, 2021.
-
Abstract
- This article proposes an automatic grammatical correction method for typos and word order errors that may occur in Chinese writing by beginners. The thesis first constructs heuristic rules and expands the corpus by analyzing the characteristics of different grammatical error types in the data set. Secondly, when the paper uses classification methods to detect grammatical errors, it extracts sentence-level binary and ternary part-of-speech combinations, n-gram models based on part-of-speech statistics and other three types of features to construct single classification and ensemble classification models, and then use convolutional neural the network constructs classification models from different angles. Finally, when the paper adopts the method based on sequence labeling for grammatical error detection, it mainly uses dependency syntax tree features, and realizes grammatical error detection by constructing a conditional random field model. This method can automatically detect grammatical errors while also identifying the sentence the location of the error. On this basis, the paper implements a simple Chinese grammatical error automatic detection system, which can provide help for the optimization of questions and answers in the question-and-answer system
- Subjects :
- Conditional random field
History
Dependency (UML)
Grammar
Computer science
Heuristic
business.industry
media_common.quotation_subject
computer.software_genre
Convolutional neural network
Sequence labeling
Computer Science Applications
Education
ComputingMethodologies_PATTERNRECOGNITION
Artificial intelligence
Abstract syntax tree
business
computer
Natural language processing
Sentence
media_common
Subjects
Details
- ISSN :
- 17426596 and 17426588
- Volume :
- 1952
- Database :
- OpenAIRE
- Journal :
- Journal of Physics: Conference Series
- Accession number :
- edsair.doi...........fc5a046b93c2b764532e3144bce63769