英语作文自动评分系统研究与实现-豆柴文库

您所在位置：网站首页 / 英语作文自动评分系统研究与实现.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

英语作文自动评分系统研究与实现
Introduction:
Overtheyears,languageassessmenthasbeenanimportantaspectofeducationalsystemsworldwide.Inthepast,languageassessmentwassolelybasedonsubjectivejudgment,whichdependedonthetestevaluator.Withtheadventofnaturallanguageprocessingandmachinelearning,automaticevaluationofwrittentextsisgainingpopularity,especiallyintheassessmentofwritingproficiency.ThepurposeofthispaperistoexplorethedevelopmentandimplementationofanEnglishessayautomaticscoringsystem.
Background:
Therehasbeenanincreasingdemandforautomatedessaygradingsystemstoaidintheevaluationofwritingproficiency.Themainreasonforthisistheneedtogradeexamsrapidlyandaccurately.Theuseofautomatedscoringsystemscanreducethetimeandcostsofgradingexams,enablingeducatorstogivemorespecificfeedbackandimprovingtheoverallqualityofeducation.
Research:
Severalstudieshavebeenconductedtodevelopautomatedessaygradingsystemswiththemostcommonmethodsbeingbasedonstatisticalandmachinelearningtechniques.Thesemodelsprocessthetextandprovidescoresbasedonfeaturessuchasgrammaticalaccuracy,vocabulary,structure,andcoherence.Researchershavealsoexploredtheuseofneuralnetworks,whichmimicthehumanbrain,andNaturalLanguageProcessingtechniques,toimplementmoresophisticatedandaccuratemodels.
Implementation:
TheimplementationoftheEnglishessayautomaticscoringsysteminvolvesseveralstages:
1.DataAcquisition:Thesystemrequiresalargedatasetofessaystotrainandvalidatethescoringmodels.Theseessayscanbeobtainedfrompreviousexamsorpublications.
2.Preprocessing:Theessaysarepreprocessedtoconvertthetextintoaformatthatcanbeusedbythemachinelearningalgorithms.Thisincludestaskssuchastokenization,stemming,lemmatization,andremovingstopwords.
3.FeatureExtraction:Thetextfeaturesthatcouldbeusedtoevaluatetheessaysareextracted.Thesefeaturescanincludewordfrequency,sentencelength,grammar,vocabulary,andcoherence.
4.ModelSelectionandTraining:Theextractedfeaturesarethenusedtotrainthemachinelearningmodels.ThemodelscanbebasedonNaïveBayes,RandomForest,SupportVectorMachinesorNeuralN