Abstract
Assessment of the descriptive paper is a tough challenge. It requires lot of man power. In educational institutions a primary hassle is evaluation of descriptive type questions & answers. Using NLP this descriptive answer assessment proper answers and the pupil answer scripts. More marks is probably awarded, if the similarity withinside the answers is more. There are several advantages of using this automated assessment device as it permits in decreasing the time concerned about the resource of the usage of faculty to correct the descriptive papers. The examinations can be accomplished online, the answers can be evaluated right now and it would be beneficial for universities, schools and schools for academic purpose with the resource of the usage of presenting ease to colleges and the examination evaluation cell.
Keyword: Descriptive answer evaluation, machine learning, natural language processing, word2vec.
References
1. Modeling of answer points to expedite semiautomated Evaluation of descriptive papers”, in proc. Int. Conf. Ieee 8th international advance computing conference (iacc), 2018, pp. 85-9.
2. aloni kadam, priyanka tarachandani, prajakta vetaln and charusheela nehete,”ai based eassessment system”, easychair preprint ,march 18, 2020.
3. Vishal bhonsle, priya sapkal, dipesh mukadam, prof. Vinit raut, “an adaptive approach for descriptive answer evaluation” viva-tech international journal for research and innovation volume 1, issue 2 (2019).
4. J. Muangprathub, s. Kajornkasirat, and a. Wanichsombat, ‘‘document plagiarism detection using a new concept similarity in formal concept analysis,’’ j. Appl. Math., vol. 2021, pp. 1–10, mar. 2021.
5. X. Hu and h. Xia, ‘‘automated assessment system for descriptive questions based on lsi,’’ in proc. 3rd int. Symp. Intell. Inf. Technol. Secur. Informat., apr. 2010, pp. 250–254.
6. M. Kusner, y. Sun, n. Kolkin, and k. Weinberger, ‘‘from word embeddings to document distances,’’ in proc. Int. Conf. Mach. Learn., 2015, pp. 957– 966.
7. C. Xia, t. He, w. Li, z. Qin, and z. Zou, ‘‘similarity analysis of law documents based on word2vec,’’ in proc. Ieee 19th int. Conf. Softw. Qual., rel. Secur. Companion (qrs-c), jul. 2019, pp. 354– 357.
8. H. Mittal and m. S. Devi, ‘‘descriptive evaluation: a comparison of several statistical techniques,’’ appl. Artif. Intell., vol. 32, no. 1, pp. 85–95, jan. 2018.
9. L. A. Cutrone and m. Chang, ‘‘automarking: automatic assessment of open questions,’’ in proc. 10th ieee int. Conf. Adv. Learn. Technol., sousse, tunisia, jul. 2010, pp. 143–147.
10. G. Srivastava, p. K. R. Maddikunta, and t. R. Gadekallu, ‘‘a two-stage text feature selection algorithm for improving text classification,’’ tech. Rep., 2021.
11. H. Mangassarian and h. Artail, ‘‘a general framework for descriptive information extraction from unstructured english text,’’ data knowl. Eng., vol. 62, no. 2, pp. 352–367, aug. 2007.
12. B. Oral, e. Emekligil, s. Arslan, and g. Eryigit, ‘‘information extractionˇ from text intensive and visually rich banking documents,’’ inf. Process. Manage., vol. 57, no. 6, nov. 2020, art. No. 102361.
13. H. Khan, m. U. Asghar, m. Z. Asghar, g. Srivastava, p. K. R. Maddikunta, and t. R.
14. Gadekallu, ‘‘fake review classification using supervised machine learning,’’ in proc. Pattern recognit. Int. Workshops challenges (icpr). New york, ny, usa: springer, 2021, pp. 269–288.
15. S. Afzal, m. Asim, a. R. Javed, m. O. Beg, and t. Baker, ‘‘urldeepdetect: a deep learning approach for detecting malicious urls using semantic vector models,’’ j. Netw. Syst. Manage., vol. 29, no. 3, pp. 1–27, mar. 2021.
16. N. Madnani and a. Cahill, ‘‘automated scoring: beyond natural language processing,’’ in proc. 27th int. Conf. Comput. Linguistics (coling), e. M. Bender, l. Derczynski, and p. Isabelle, eds. Santa fe, nm, usa: association for computational linguistics, aug. 2018, pp. 1099–1109.
17. Z. Lin, h. Wang, and s. I. Mcclean, ‘‘measuring tree similarity for natural language processing based information retrieval,’’ in proc. Int. Conf. Appl. Natural lang. Inf. Syst. (nldb) (lecture notes in computer science), vol. 6177, c. J. Hopfe, y. Rezgui, e. Métais, a. D. Preece, and h. Li, eds. Cardiff, u.k.: springer, 2010, pp. 13–23.
18. G. Grefenstette, ‘‘tokenization,’’ in syntactic wordclass tagging. Springer, 1999, pp. 117–133.
19. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776(2019). https://doi.org/10.1007/s10586-018-1757-3
20. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z.
21. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.
22. V.Senthilkumar , K.Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
23. Dr.Vignesh Janarthanan, Dr.Venkata Reddy Medikonda,.Er.Dr.G.Manoj Someswar Proposal of a Novel Approach for Stabilization of the Image from Omni-Directional System in the case of Human Detection & Tracking “American Journal of Engineering Research (AJER)” vol 6 issue 11 2017
24. Sowmitha, V., and Mr V. Senthilkumar. "A Cluster Based Weighted Rendezvous Planning for Efficient Mobile-Sink Path Selection in WSN." International Journal for Scientific Research & Development Vol 2 Issue 11 2015
25. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
26. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
27. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
28. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
29. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
30. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
31. Ardra, S., & Viswanathan, A. (2012). A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).
32. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
33. S.B. Jayabharathi, S. Manjula, E. Tamil Selvan, P. Vengatesh and V. Senthil Kumar” Semantic Risk Analysis Model Cancer Data Prediction” Journal on Science Engineering and Technology Vo1ume 5, No. 02, April 2018