Automated short answer scoring

Abstract

In 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.