Optimization of Window Size for Calculating Semantic Coherence Within an Essay
Abstract Over the last fifty years, as the field of automated essay evaluation has progressed, several ways have been offered. The three aspects of style, substance, and semantics are the primary focus of automated essay evaluation. The style and content attributes have received the most attention, while the semantics attribute has received less attention. A smaller fraction of the essay (window) is chosen to measure semantics, and the essay is broken into smaller portions using this window. The goal of this work is to determine an acceptable window size for measuring semantic coherence between different parts of the essay with more precision.
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Hearst, M. A. (1997). Text Tiling: Segmenting text into multi-paragraph subtopic passages. Computational linguistics, 23(1), 33–64.
Injadat, M., Moubayed, A., Nassif, A. B., and Shami, A. (2020). Systematic ensemble model selection approach for educational data mining. Knowledge-Based Systems, 200, 105992.
Janda, H. K., Pawar, A., Du, S., and Mago, V. (2019). Syntactic, semantic and sentiment analysis: The joint effect on automated essay evaluation. IEEE Access, 7, 108486–108503.
J. Burstein, J. Tetreault, and S. Andreyev (2010).«Using Entity-Based Features to Model Coherence in Student Essays», in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, no. June. Los Angeles, California: Association for Computational Linguistics, pp. 681–684.
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Misuraca, M., Scepi, G., and Spano, M. (2021). Using Opinion Mining as an educational analytic: An integrated strategy for the analysis of students’ feedback. Studies in Educational Evaluation, 68, 100979.
Muangkammuen, P., and Fukumoto, F. (2020). Multi-task Learning for Automated Essay Scoring with Sentiment Analysis. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, pp. 116–123.
P. J. Clark and F. C. Evans (1954) «Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations», Ecology, vol. 35, no. 4, pp. 445–453.
P. W. Foltz, L. A. Streeter, K. E. Lochbaum, and T. K. Landauer (2013). «Implementation and Applications of the Intelligent Essay Assessor», in Handbook of Automated Essay Evaluation: Current Applications and New Directions, M. D. Shermis and J. Burstein, Eds. New York: Routledge, ch. 5, pp. 68–88.
P. W. Foltz (2007). «Discourse Coherence and LSA», in Handbook of Latent Semantic Analysis, T. K. Landauer, D. S. McNamara, S. Dennis, and W. Kintsch, Eds. Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc., ch. 9, pp. 167–184.
Peng, C., Chen, Y., Kang, Z., Chen, C., and Cheng, Q. (2020). Robust principal component analysis: A factorization-based approach with linear complexity. Information Sciences, 513, 581–599.
Psalmerosi, F. H. (2019). Applying Text Mining and Machine Learning to Build Methods for Automated Grading (Master’s thesis, University of Twente).
Romero, C., and Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, v10.
Srivastava K., Dhanda N. & Shrivastava A. (2020), An Analysis of Automated Essay Grading Systems, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-6, March 2020.
T. Kakkonen, N. Myller, E. Sutinen, and J. Timonen (2008). «Comparison of Dimension Reduction Methods for Automated Essay Grading», Educational Technology & Society, v. 11, pp. 275–288.
T. K. Landauer, P. W. Foltz, and D. Laham (1998). «An introduction to latent semantic analysis», Discourse Processes, vol. 25, pp. 259–284.
Y. Attali, (2011). «A Differential Word Use Measure for Content Analysis in Automated Essay Scoring», ETS Research Report Series, vol. 36.
Zupanc, K., and Bosni?, Z. (2017). Automated essay evaluation with semantic analysis. Knowledge-Based Systems, 120, 118–132.
Zupanc, K., and Bosnic, Z. (2014). Automated essay evaluation augmented with semantic coherence measures. In 2014 IEEE International Conference on Data Mining, IEEE, pp. 1133–1138.
Zupanc, K., and Bosnic, Z. (2016). Advances in the field of automated essay evaluation. Informatica, 39(4).
Azmi, A. M., Al-Jouie, M. F., and Hussain, M. (2019). AAEE–Automated evaluation of students’ essays in Arabic language. Information Processing & Management, 56(5), 1736–1752.
Bhatt, R., Patel, M., Srivastava, G., and Mago, V. (2020). A Graph Based Approach to Automate Essay Evaluation. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4379–4385.
Chandrasekaran, D., and Mago, V. (2020). Evolution of Semantic Similarity--A Survey. arXiv preprint arXiv:2004.13820.
D. Higgins, J. Burstein, D. Marcu, and C. Gentile, (2004).«Evaluating Multiple Aspects of Coherence in Student Essays», in Proceedings of HLT-NAACL, Boston, MA.
Darwish, S. M., and Mohamed, S. K. (2019). Automated Essay Evaluation Based on Fusion of Fuzzy Ontology and Latent Semantic Analysis. In International Conference on Advanced Machine Learning Technologies and Applications, pp. 566–575.
Ferreira?Mello, R., André, M., Pinheiro, A., Costa, E., and Romero, C. (2019). Text mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.
Foltz, P. W. (2007). Discourse coherence and LSA. Handbook of latent semantic analysis, 167-184.
Foltz, P. W., Laham, D., and Landauer, T. K. (1999). The intelligent essay assessor: Applications to educational technology. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 1(2), 939–944.
Gao, Y., Davies, P. M., and Passonneau, R. J. (2018). Automated content analysis: A case study of computer science student summaries. In Proceedings of the thirteenth workshop on innovative use of NLP for building educational applications, pp. 264–272.
Goulart, H. X., Tosi, M. D., Gonçalves, D. S., Maia, R. F., and Wachs-Lopes, G. A. (2018). Hybrid model for word prediction using naive bayes and latent information. arXiv preprint arXiv:1803.00985.
Grosz, B. J., Joshi, A. K., and Weinstein, S. (1995). Centering: A framework for modelling the local coherence of discourse.
Hearst, M. A. (1997). Text Tiling: Segmenting text into multi-paragraph subtopic passages. Computational linguistics, 23(1), 33–64.
Injadat, M., Moubayed, A., Nassif, A. B., and Shami, A. (2020). Systematic ensemble model selection approach for educational data mining. Knowledge-Based Systems, 200, 105992.
Janda, H. K., Pawar, A., Du, S., and Mago, V. (2019). Syntactic, semantic and sentiment analysis: The joint effect on automated essay evaluation. IEEE Access, 7, 108486–108503.
J. Burstein, J. Tetreault, and S. Andreyev (2010).«Using Entity-Based Features to Model Coherence in Student Essays», in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, no. June. Los Angeles, California: Association for Computational Linguistics, pp. 681–684.
J. Burstein, J. Tetreault, and N. Madnani (2013), «The E-rater Automated Essay Scoring System», in Handbook of Automated Essay Evaluation: Current Applications and New Directions, M. D. Shermis and J. Burstein, Eds. New York Routledge, 2013, ch. 4, pp. 55–67.
Khatavkar, V., and Kulkarni, P. (2019). Trends in Document Analysis. In Data Management, Analytics and Innovation (pp. 249–262). Springer, Singapore.
Ke, Z., and Ng, V. (2019). Automated essay scoring: a survey of the state of the art. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (pp. 6300–6308). AAAI Press.
M. D. Shermis and J. Burstein (2003) «Introduction», in Automated essay scoring: A cross-disciplinary perspective, M. D. Shermis, and J. Burstein, Eds. Manwah, NJ: Lawrence Erlbaum Associates, 2003, pp. 13–16.
M. D. Shermis and B. Hamner, (2013). «Contrasting State-of-the-Art Automated Scoring of Essays: Analysis», in Handbook of Automated Essay Evaluation: Current Applications and New Directions, M. D. Shermis and J. Burstein, Eds. New York: Routledge, ch. 19, pp. 313–346.
M. T. Schultz (2013) «The IntelliMetric Automated Essay Scoring Engine - A Review and an Application to Chinese Essay Scoring», in Handbook of Automated Essay Evaluation: Current Applications and New Directions, M. D. Shermis and J. C. Burstein, Eds. New York: Routledge, 2013, ch. 6, pp. 89–98.
Mayfield, E., and Rosé, C. (2010, June). An interactive tool for supporting error analysis for text mining. In Proceedings of the NAACL HLT 2010 Demonstration Session (pp. 25–28).
Mimno, D., Wallach, H., Talley, E., Leenders, M., and McCallum, A. (2011). Optimizing semantic coherence in topic models. In Proceedings of the 2011 conference on empirical methods in natural language processing (pp. 262–272).
Misuraca, M., Scepi, G., and Spano, M. (2021). Using Opinion Mining as an educational analytic: An integrated strategy for the analysis of students’ feedback. Studies in Educational Evaluation, 68, 100979.
Muangkammuen, P., and Fukumoto, F. (2020). Multi-task Learning for Automated Essay Scoring with Sentiment Analysis. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, pp. 116–123.
P. J. Clark and F. C. Evans (1954) «Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations», Ecology, vol. 35, no. 4, pp. 445–453.
P. W. Foltz, L. A. Streeter, K. E. Lochbaum, and T. K. Landauer (2013). «Implementation and Applications of the Intelligent Essay Assessor», in Handbook of Automated Essay Evaluation: Current Applications and New Directions, M. D. Shermis and J. Burstein, Eds. New York: Routledge, ch. 5, pp. 68–88.
P. W. Foltz (2007). «Discourse Coherence and LSA», in Handbook of Latent Semantic Analysis, T. K. Landauer, D. S. McNamara, S. Dennis, and W. Kintsch, Eds. Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc., ch. 9, pp. 167–184.
Peng, C., Chen, Y., Kang, Z., Chen, C., and Cheng, Q. (2020). Robust principal component analysis: A factorization-based approach with linear complexity. Information Sciences, 513, 581–599.
Psalmerosi, F. H. (2019). Applying Text Mining and Machine Learning to Build Methods for Automated Grading (Master’s thesis, University of Twente).
Romero, C., and Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, v10.
Srivastava K., Dhanda N. & Shrivastava A. (2020), An Analysis of Automated Essay Grading Systems, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-6, March 2020.
T. Kakkonen, N. Myller, E. Sutinen, and J. Timonen (2008). «Comparison of Dimension Reduction Methods for Automated Essay Grading», Educational Technology & Society, v. 11, pp. 275–288.
T. K. Landauer, P. W. Foltz, and D. Laham (1998). «An introduction to latent semantic analysis», Discourse Processes, vol. 25, pp. 259–284.
Y. Attali, (2011). «A Differential Word Use Measure for Content Analysis in Automated Essay Scoring», ETS Research Report Series, vol. 36.
Zupanc, K., and Bosni?, Z. (2017). Automated essay evaluation with semantic analysis. Knowledge-Based Systems, 120, 118–132.
Zupanc, K., and Bosnic, Z. (2014). Automated essay evaluation augmented with semantic coherence measures. In 2014 IEEE International Conference on Data Mining, IEEE, pp. 1133–1138.
Zupanc, K., and Bosnic, Z. (2016). Advances in the field of automated essay evaluation. Informatica, 39(4).
Srivastava, K., Dhanda, . N. . ., & Shrivastava, A. (2022). Optimization of Window Size for Calculating Semantic Coherence Within an Essay. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(2), 147–158. https://doi.org/10.14201/adcaij.27184
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