Short question-answers assessment using lexical and semantic similarity based features

References

• M. Mohler, et al. “Learning to grade short answer questions using semantic similarity measures and dependency graph alignments,” In Proceedings of the 49th annual meeting of ACL: HLT (2011): 752-762.
   Google Scholar
• S. Saha, et al. “Sentence level or token level features for automatic short answer grading?: Use both”, in proceedings of ICAIE, (2018) 503-517.
   Google Scholar
• T. Mitchel, et al. “Towards robust computerised marking of free-text responses,” 2002.
   Google Scholar
• C. Leacock and M. Chodorow, “C-rater: Automated scoring of short-answer questions,” Computers and the Humanities, 37.4 (2003): 389-405. doi: 10.1023/A:1025779619903
   Google Scholar
• O. Bukai, et al. “An automated short-free-text scoring system: development and assessment”, In Proceedings of the 20th IITSE Conference. (2006): 1-11.
   Google Scholar
• M. Mohler and R. Mihalcea, “Text-to-text semantic similarity for automatic short answer grading,” In Proceedings of the 12th Conference of the European Chapter of the ACL, (2009): 567-575.
   Google Scholar
• V. Nandini, and P. U. Maheswari, “Automatic assessment of descriptive answers in online examination system using semantic relational features”, The Journal of Supercomputing, 76.6 (2020): 4430-4448. doi: 10.1007/s11227-018-2381-y
   Web of Science ®Google Scholar
• L. Ramachandran, et al. “Identifying patterns for short answer scoring using graph-based lexico-semantic text matching”, In Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications (2015) 97-106.
   Google Scholar
• A. Sahu, and P. K. Bhowmick, “Feature Engineering and Ensemble-based Approach for Improving Automatic Short-answer Grading Performance”, IEEE Transactions on Learning Technologies, 13.1 (2019): 77-90. doi: 10.1109/TLT.2019.2897997
   Web of Science ®Google Scholar
• S. Kumar, et al. “Earth Mover’s Distance Pooling over Siamese LSTMs for Automatic Short Answer Grading”, In IJCAI, (2017): 2046-2052.
   Google Scholar
• M. A. Sultan, et al. “Fast and easy short answer grading with high accuracy”, In Proceedings of the 2016 Conference of the North American Chapter of the ACL: HLT, (2016): 1070-1075. 2016.
   Google Scholar
• J. Turian, et al. “Word representations: a simple and general method for semi-supervised learning”, In Proceedings of the 48th annual meeting of the association for computational linguistics, (2010): 384-394.
   Google Scholar
• N. Meinshausen, and P. Bühlmann, “Stability selection”, Journal of the Royal Statistical Society: Series B, 72.4 (2010): 417-473. doi: 10.1111/j.1467-9868.2010.00740.x
   Google Scholar
• I. Guyon, et al. “Gene selection for cancer classification using support vector machines, Machine learning”, 46.1,(2002): 389-422.
   Google Scholar
• H. Drucker, et al. “Support vector regression machines”, In Advances in neural information processing systems, (1997): 155-161.
   Google Scholar
• G. A. F. Seber, et al. “Linear regression analysis”, 2nd ed. New York, NY, United States: Wiley, John & Sons, (2003).
   Google Scholar
• J. Mendes-Moreira, et al. “Ensemble approaches for regression: A survey”, Acm computing surveys (csur), 45.1 (2012). doi: 10.1145/2379776.2379786
   Web of Science ®Google Scholar
• P. Ahlgren, et al. “Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient”, Journal of the ASIST, 54.6 (2003): 550-560.
   Web of Science ®Google Scholar
• T. Ahmad and N. Ahmad, “A Simple Guide to Implement Data Retrieval through Natural Language Database Query Interface (NLDQ),” in proceedings of 2019 SMART, (2019): 37-41.
   Google Scholar
• M. S. Shaukat, et al. “Semantic Similarity based Descriptive Answer Evaluation,” In Web Semantics: Cutting Edge and Future Directions in Health Care, Sarika Jain, Ed.; Elsevier, (2021): 221-231.
   Google Scholar
• Verma, Vivek Kumar, et al. “Dissecting word embeddings and language models in natural language processing.” Journal of Discrete Mathematical Sciences and Cryptography 24.5 (2021): 1509-1515. doi: 10.1080/09720529.2021.1968108
   Web of Science ®Google Scholar