Burrows, S., Gurevych, I., & Stein, B. (2015). The eras and trends of automatic short answer grading. International journal of artificial intelligence in education
Filighera, A., Steuer, T., & Rensing, C. (2020, July). Fooling automatic short answer grading systems. International conference on artificial intelligence in education
(pp. 177-190). Cham: Springer. https://doi.org/10.1007/978-3-030-52237-7_15
Gabrilovich, E., & Markovitch, S. (2009). Wikipedia-based semantic interpretation for natural language processing. Journal of artificial intelligence research
Hirst, G., & St-Onge, D. (1998). Lexical chains as representations of context for the detection and correction of malapropisms. WordNet: An electronic lexical database, 305, 305-332.
Jarmasz, M., & Szpakowicz, S. (2003). Roget’s thesaurus and semantic similarity. In N. Nicolov., K. Bontcheva., G. Angelova., & R. Mitkov (Eds.), Recent advances in natural language processing iii.John Benjamins Publishing Co.
Jarmasz, M., & Szpakowicz, S. (2012). Roget's Thesaurus and semantic similarity. Proceedings of conference on recent advances in natural language processing. arXiv:1204.0245
Jiang, J. J., & Conrath, D. W. (1997). Semantic similarity based on corpus statistics and lexical taxonomy. arXiv preprint cmp-lg/9709008.
Leacock, C., & Chodorow, M. (1998). Combining local context and WordNet similarity for word sense identification. WordNet: An electronic lexical database, 49(2), 265-283.
Lee, Y. Y., Ke, H., Yen, T. Y., Huang, H. H., & Chen, H. H. (2020). Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement. Journal of the association for information science and technology
(6), 657-670. https://doi.org/10.1002/asi.24289
Lesk, M. (1986, June). Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. Proceedings of the 5th annual international conference on systems documentation,
Li, P., Xiao, B., Ma, W., Jiang, Y., & Zhang, Z. (2017). A graph-based semantic relatedness assessment method combining wikipedia features. Engineering applications of artificial intelligence
, 268-281. https://doi.org/10.1016/j.engappai.2017.07.027
Mihalcea, R., Corley, C., & Strapparava, C. (2006, July). Corpus-based and knowledge-based measures of text semantic similarity. Proceedings of the 21st national conference on artificial intelligence
(pp. 775-780). https://dl.acm.org/doi/10.5555/1597538.1597662
Mohler, M., & Mihalcea, R. (2009, March). Text-to-text semantic similarity for automatic short answer grading. The 12th conference of the european chapter of the ACL (pp. 567-575). Athens, Greece. DOI: 10.3115/1609067.1609130
Mohler, M., Bunescu, R., & Mihalcea, R. (2011, June). Learning to grade short answer questions using semantic similarity measures and dependency graph alignments. The 49th annual meeting of the association for computational linguistics: Human language technologies
(pp. 752-762). https://dl.acm.org/doi/10.5555/2002472.2002568
Nazari Soleimandarabi, M., Mirroshandel, S. A., & Sadr, H. (2015a). The significance of semantic relatedness and similarity measures in geographic information science. International journal of computer science and network solutions, 3(2), 12-23.
Nazari Soleimandarabi, M., Mirroshandel, S. A., & Sadr, H. (2015b). A Survey of semantic relatedness measures. International journal of computer science and network solutions, 3(2), 1-11.
Patwardhan, S., & Pedersen, T. (2006). Using WordNet-based context vectors to estimate the semantic relatedness of concepts. The workshop on making sense of sense: bringing psycholinguistics and computational linguistics together.
Pedersen, T., Patwardhan, S., & Michelizzi, J. (2004, July). WordNet:: similarity-measuring the relatedness of concepts. HLT-NAACL--Demonstrations '04: Demonstration Papers at HLT-NAACL 2004 (pp. 38–41). Association for Computational Linguistics.
Peinelt, N., Nguyen, D., & Liakata, M. (2020, July). tBERT: Topic models and BERT joining forces for semantic similarity detection. Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 7047-7055).
Resnik, P. (1995). Using information content to evaluate semantic similarity in a taxonomy. arXiv preprint cmp-lg/9511007
Roitman, H., & Kurland, O. (2019, July). Query performance prediction for pseudo-feedback-based retrieval. The 42nd international ACM SIGIR conference on research and development in information retrieval
(pp. 1261-1264). https://doi.org/10.1145/3331184.3331369
Roy, S., Rajkumar, A., & Narahari, Y. (2018). Selection of automatic short answer grading techniques using contextual bandits for different evaluation measures. International journal of advances in engineering sciences and applied mathematics
(1), 105-113. https://doi.org/10.1007/s12572-017-0202-9
Sadr, H., & Nazari Solimandarabi, M. (2019). Presentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures. Journal of advances in computer research, 10(2), 17-30.
Sadr, H., Nazari, M., Pedram, M. M., & Teshnehlab, M. (2019a). Exploring the efficiency of topic-based models in computing semantic relatedness of geographic terms. International journal of web research, 2(2), 23-35.
Sadr, H., Pedram, M. M., & Teshnehlab, M. (2019c). A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks. Neural processing letters
(3), 2745-2761. https://doi.org/10.1007/s11063-019-10049-1
Sadr, H., Pedram, M. M., & Teshnehlab, M. (2020). Multi-view deep network: A deep model based on learning features from heterogeneous neural networks for sentiment analysis. IEEE access
Sadr, H., Pedram, M. M., & Teshnehlab, M. (2021). Convolutional neural network equipped with attention mechanism and transfer learning for enhancing performance of sentiment analysis. Journal of AI and data mining. 9(2), 141-151. DOI:10.22044/jadm.2021.9618.2100
Sadr, H., Pedram, M. M., & Teshnelab, M. (2019b). Improving the performance of text sentiment analysis using deep convolutional neural network integrated with hierarchical attention layer. International journal of information and communication technology research
(3), 57-67. http://ijict.itrc.ac.ir/article-1-416-en.html
Shermis, M. D., & Burstein, J. (Eds.). (2013). Handbook of automated essay evaluation: Current applications and new directions. Routledge.
Strube, M., & Ponzetto, S. P. (2006, July). WikiRelate! Computing semantic relatedness using Wikipedia. AAAI'06 Proceedings of the 21st national conference on Artificial intelligence
(pp. 1419-1424). https://dl.acm.org/doi/10.5555/1597348.1597414
Süzen, N., Gorban, A. N., Levesley, J., & Mirkes, E. M. (2020). Automatic short answer grading and feedback using text mining methods. Procedia computer science
, 726-743. https://doi.org/10.1016/j.procs.2020.02.171
Taieb, M. A. H., Zesch, T., & Aouicha, M. B. (2020). A survey of semantic relatedness evaluation datasets and procedures. Artificial intelligence review
(6), 4407-4448. https://doi.org/10.1007/s10462-019-09796-3
Witten, I. H., & Milne, D. N. (2008). An effective, low-cost measure of semantic relatedness obtained from Wikipedia links.
Wu, Z., & Palmer, M. (1994). Verbs semantics and lexical selection. The 32nd annual meeting on association for computational linguistics. arXiv preprint cmp-lg/9406033.
Young, J. R. (2012). Inside the Coursera contract: How an upstart company might profit from free courses. The chronicle of higher education, 19(07), 2012.
Zesch, T., & Gurevych, I. (2010). Wisdom of crowds versus wisdom of linguists–measuring the semantic relatedness of words. Natural language engineering, 16(1), 25-59.
Zhang, L., Huang, Y., Yang, X., Yu, S., & Zhuang, F. (2019). An automatic short-answer grading model for semi-open-ended questions. Interactive learning environments
, 1-14. https://doi.org/10.1080/10494820.2019.1648300
Zhang, Y., Lin, C., & Chi, M. (2020). Going deeper: Automatic short-answer grading by combining student and question models. User modeling and user-adapted interaction
(1), 51-80. https://doi.org/10.1007/s11257-019-09251-6
Zhang, Z., Gentile, A. L., & Ciravegna, F. (2013). Recent advances in methods of lexical semantic relatedness–a survey. Natural language engineering, 19(4), 411-479.
Zhu, X., Guo, Q., Zhang, B., & Li, F. (2019). An efficient approach for measuring semantic relatedness using Wikipedia bidirectional links. Applied intelligence
(10), 3708-3730. https://doi.org/10.1007/s10489-019-01452-1