Open AccessAn Errors Correction Model for the Errors of Non-word and Real-word in English Composition
Author(s) -
Guimin Huang Guimin Huang,
Maolin Li Guimin Huang
Publication year - 2022
Publication title -
diànnǎo xuékān/diannao xuekan
Language(s) - English
Resource type - Journals
eISSN - 2312-993X
pISSN - 1991-1599
DOI - 10.53106/199115992022023301013
Subject(s) - word (group theory) , computer science , natural language processing , artificial intelligence , set (abstract data type) , speech recognition , word error rate , generalization , error detection and correction , matching (statistics) , algorithm , mathematics , statistics , mathematical analysis , geometry , programming language
In the procedure from composing English, it is inevitable to face the phenomenon of word writing errors. In recent years, English composition automatic correcting system has attracted much attention. However, the precision of the existing word errors correcting system is vague generalization. So as to move forward the accuracy of checking and correcting word errors, this paper designs a word errors correction model based on natural language processing technology. This model designs phoneme matching method based on an improved IDM algorithm, and combined with a non-word input errors correction method based on character distance. The accuracy of correcting non-word errors in this model reached 86.5%. The study also proposes a real-word errors correction method, which is implemented basing on the real-word confusion set and combining the binary statistical model and the GloVe word vector model, improving the real-word errors correction method based on feature annotation of the real-word confusion set, with an accuracy of 77.9%.