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dc.contributor.advisorMostakim, Moin
dc.contributor.advisorAbdullah, Matin Saad
dc.contributor.authorMithu, M. Rayhan Ahmed
dc.contributor.authorRobin, Syed Mohammad Moinul Islam
dc.contributor.authorKamal, Moumita
dc.date.accessioned2017-01-16T05:42:47Z
dc.date.available2017-01-16T05:42:47Z
dc.date.copyright2016
dc.date.issued2016-12-14
dc.identifier.otherID 12101115
dc.identifier.otherID 13201082
dc.identifier.otherID 12141001
dc.identifier.urihttp://hdl.handle.net/10361/7600
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 62-64).
dc.description.abstractIn our thesis we have worked to analyses text short answers then predict the score accordingly by using different extracted features. In our research we have used around 1700 data for each dataset and are scored by two different humans provided by the Hewlett foundation available in Kaggle. We have used different NLP techniques to process the data in order to use it to the classifiers. Sckit was used to implement the algorithms of the different classifiers. The data were divided into two different data sets, one of them was the training set and the test set. The training set data was used to train the different classifiers afterwards the test set data was given to the classifiers to predict the score. The predicted score was compared with the score given by the humans to find the efficiency and accuracy of the different classifiers.en_US
dc.description.statementofresponsibilityM. Rayhan Ahmed Mithu
dc.description.statementofresponsibilityMoumita Kamal
dc.description.statementofresponsibilitySyed Mohammad Moinul Islam Robin
dc.format.extent64 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectNLP techniquesen_US
dc.subjectAutomated scoringen_US
dc.subjectShort answeren_US
dc.titleAutomated short answer scoringen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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