AutomaticEssayGrading

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AutomaticEssayGrading

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Automatic Essay Grading algorithms using machine learning.

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# Automatic Essay Grading

In the task of essay grading we assign a grade to an essay in the most human-like manner possible. We tackle this problem by building statistical models which try and predict how a human would evaluate an essay. In this paper, the models we use are Support Vector Machines, Decision Trees, Random forests and Recurrent Neural Networks (LSTMs). We also propose to use the technique of boosting with Decision Trees.

# References

- Murray, and Orii “Automatic Essay Scoring” http://www.cs.cmu.edu/~norii/pub/aes.pdf

- Mahana, Johns and Apte “Automatic Essay Grading with Machine Learning”   http://cs229.stanford.edu/proj2012/MahanaJohnsApte-AutomatedEssayGradingUsingMachineLearning.pdf

- AnalyticsVidhya “Tutorial on Tree based Modelling” 		https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/#one

- Attali, Yigal, and Jill Burstein. “Automated essay scoring with e-rater V. 2.” The Journal of Technology, Learning and Assessment 4.3 (2006). https://www.ets.org/Media/Research/pdf/RR-04-45.pdf
- Kaggle. “Develop an automated scoring algorithm for student-written essays.” (2012). https://www.kaggle.com/c/asap-aes 
- Robertson, Stephen. “Understanding inverse document frequency: on theoretical arguments for IDF.” Journal of documentation 60.5 (2004): 503-520. 
- Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. “Glove: Global Vectors for Word Representation.” EMNLP. Vol. 14. 2014. 10 
- Huyenn and Lucio Dery “Neural Networks for Automated Essay Grading” https://cs224d.stanford.edu/reports/huyenn.pdf
- Hochreiter, Sepp, and Jrgen Schmidhuber. ”Long short-term memory.” Neural computation 9.8 (1997): 1735-1780
- Colah “Understanding LSTMs” http://colah.github.io/posts/2015-08-Understanding-LSTMs/

AutomaticEssayGrading

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