Automated Essay Evaluation using Chart Parser
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10.5120/ijca2021921357 |
Sampada K.S., Anusha S. and Vignesh N Karthik. Automated Essay Evaluation using Chart Parser. International Journal of Computer Applications 183(7):15-18, June 2021. BibTeX
@article{10.5120/ijca2021921357,
author = {Sampada K.S. and Anusha S. and N. Vignesh Karthik},
title = {Automated Essay Evaluation using Chart Parser},
journal = {International Journal of Computer Applications},
issue_date = {June 2021},
volume = {183},
number = {7},
month = {Jun},
year = {2021},
issn = {0975-8887},
pages = {15-18},
numpages = {4},
url = {http://www.ijcaonline.org/archives/volume183/number7/31939-2021921357},
doi = {10.5120/ijca2021921357},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
Abstract
Essays help in assessing academic excellence and linking various ideas with the ability to recall. Evaluating essays manually is a tedious and time consuming job. Automated grading shall reduce the evaluation time and with appropriate training, would generate a realistic and accurate score. We aim to develop an automated essay evaluation system by employing a regressor, fed with features like count of misspelt words, sentences, words, characters, nouns, verbs, adverbs, adjectives, and lemmas. Sentences are checked for grammatical correctness using a custom built parser. The regressors are trained on the enlisted features and then measured for the performance. Various regressors like Linear, Logistic and Random Forest have been employed and observed to select a model with the best performance for use.
References
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Keywords
Natural language Processing, Machine Learning, NLTK POS-tagger, Chart Parser, Linear Regression, Logistic Regression, Random Forest Regression. .