Bhushan Nandwalkar*, Sukruta Pardeshi, Makarand Shahade, and Ashish Awate
|  Rahman, M.M. and Akter, F.An Automated Approach for Answer Script Evaluation Using Natural Language Processing.
 Rani A.M.Automated Explanatory Answer Evaluation Using Machine Learning Approach.
 Nandini, V. and Uma Maheswari, P. Automatic assessment of descriptive answers in online examination system using semantic relational features.
 Singh S., Shah Y., Vajani Y., andDholay S.Automated Paper Evaluation System for Subjective Handwritten Answers. In
 Wu, K., Fu, H. and Li, W.Handwriting Text-line Detection and Recognition in Answer Sheet Composition with Few Labeled Data. In
 Mule, H., Kadam, N. and Naik, D.Handwritten Text Recognition from an Image with Android Application. In
 Sijimol, P.J. and Varghese, S.M.Handwritten short answer evaluation system (HSAES).
 Jayasiriwardene, T.D. and Ganegoda, G.U.Keyword extraction from Tweets using NLP tools for collecting relevant news. In
 Surana S., Pathak K., Gagnani M., Shrivastava V. and Mahesh T.R.Text Extraction and Detection from Images using Machine Learning Techniques: A Research Review. In
 Zhuohao W.A.N.G., Dong, W.A.N.G. and Qing, L.I. Keyword Extraction from Scientific Research Projects Based on SRP‐TF‐IDF.
 Wang Y., Zhang D., Yuan Y., Liu Q. and Yang Y.Improvement of TF-IDF algorithm based on knowledge graph. In
 Pang S., Yao J., Liu T., Zhao H. and Chen H.A text similarity measurement based on semantic fingerprint of characteristic phrases.
 Peinelt, N., Nguyen, D. and Liakata, M. tBERT: Topic models and BERT joining forces for semantic similarity detection. In
 Viji, D. and Revathy, S.A hybrid approach of Weighted Fine-Tuned BERT extraction with deep Siamese Bi-LSTM model for semantic text similarity identification.
 Devlin J., Chang M.W., Lee K. and Toutanova K.Bert: Pre-training of deep bidirectional transformers for language understanding.arXiv preprint arXiv:1810.04805, 2018.
 Rahman, M.M. and Akter, F.An Automated Approach for Answer Script Evaluation Using Natural Language Processing.
 Hsu W.L., Chen Y.S., Shiau Y.C., Liu H.L. and Chern T.Y.Curriculum design in construction engineering departments for colleges in taiwan.
 Ivanova, V. and Zlatanov, B.Implementation of fuzzy functions aimed at fairer grading of students’ tests.
 Pandey, M., Srivastava, P.K. and Bhattacharjee, V.Fuzzy logic based grading system for student projects using quality attributes.
 Hameed, I.A. and Sorensen, C.G.Fuzzy systems in education: a more reliable system for student evaluation, ISBN, pp. 978-953, 2010.
 McLoone, S.C. On using fuzzy logic for grading highly subjective assessment material-a case study, 2012.
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