Abstract
Automatic essay scoring has been shown to be an effective mechanism for quickly assessing student responses in the education system. It has already a wide variety of applications to solve, but there are evaluating the essays based on statistical features like Bag of Words (BoG), Term Frequency-Inverse Document Frequency (TF-IDF). Some of the evaluating approaches are considering the features like Word embedding with Glove, Word2Vec, One hot encoding. Both types of approaches are not fulfilling essay evaluation and not able to retrieve semantic information from essays. Here we are evaluating the essay with Word2Vec and Long Short-Term Memory (LSTM) with K-Fold cross-validation and we got an accuracy of 85.35.
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Ramesh, D., Sanampudi, S.K. (2022). An Improved Approach for Automated Essay Scoring with LSTM and Word Embedding. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_4
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