Text Classification of Conversational Implicatures Based on Lexical Features

References

• Ahmad, F., and M. Faisal. 2022. A novel hybrid methodology for computing semantic similarity between sentences through various word senses. International Journal of Cognitive Computing in Engineering 3:58–3175. doi:10.1016/j.ijcce.2022.02.001.
   Google Scholar
• Cappelen, H., and E. Lepore. 2005. Insensitive semantics: A defense of semantic minimalism and speech act pluralism. Oxford: Blackwell Publishing Ltd.
   Google Scholar
• Clark, B. 2013. Relevance theory. Cambridge: Cambridge University Press.
   Google Scholar
• Feng, W.-S., H.-B. Yu, and X.-L. Zhou. 2021. Understanding particularized and generalized conversational implicatures: Is theory-of-mind necessary? Brain and Language 212:104878. doi:10.1016/j.bandl.2020.104878.
   PubMed Web of Science ®Google Scholar
• Frank, C., and N. Goodman. 2012. Predicting pragmatic reasoning in language games. Science 336 (6084):998. doi:10.1126/science.1218633.
   PubMed Web of Science ®Google Scholar
• Garassino, D., N. Brocca, and V. Masia. 2022. Is implicit communication quantifiable? A corpus-based analysis of British and Italian political tweets. Journal of Pragmatics 194:9–22. doi:10.1016/j.pragma.2022.03.024.
   Web of Science ®Google Scholar
• George, J., and R. Mamidi. 2020. Conversational implicatures in English dialogue: Annotated dataset. Procedia Computer Science 171:2316–23. doi:10.1016/j.procs.2020.04.251.
   Google Scholar
• Goodman, D., and M. Frank. 2016. Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences 20 (11):818–29. doi:10.1016/j.tics.2016.08.005.
   PubMed Web of Science ®Google Scholar
• Graesser, C., D. McNamara, Z.-Q. Cai, M. Conley, H.-Y. Li, and J. Pennebaker. 2014. COH-METRIX MEASURES TEXT CHARACTERISTICS at MULTIPLE LEVELS of LANGUAGE and DISCOURSE. The Elementary School Journal 115 (2):210–29. doi:10.1086/678293.
   Web of Science ®Google Scholar
• Graesser, C., D. McNamara, and J. Kulikowich. 2011. Coh-Metrix: Providing multilevel analyses of text characteristics. Educational Researcher 40 (5):223–34. doi:10.3102/0013189x11413260.
   Web of Science ®Google Scholar
• Graesser, C., D. McNamara, M. Louwerse, and Z.-Q. Cai. 2004. Coh-Metrix: Analysis of text on cohesion and language. Behavior Research Methods Instruments & Computers 36 (2):193–202. doi:10.3758/bf03195564.
   PubMedGoogle Scholar
• Hoek, J., and M. Scholman. 2017. Evaluating discourse annotation: Some recent insights and new approaches.
   Google Scholar
• Huang, Y. 2014. Pragmatics. 2 ed. Oxford: Oxford University Press.
   Google Scholar
• Huang, Y. 2017. Implicature. In The Oxford Handbook of Pragmatics, ed. Y. Huang, pp. 155–79. Oxford: Oxford University Press.
   Google Scholar
• Kavanagh, R. 2022.Fairness and communication-based semantics for session-typed languages. Information and Computation 285: 104892.doi: https://doi.org/10.1016/j.ic.2022.104892
   Web of Science ®Google Scholar
• Kecskes, I. 2008. Dueling contexts: A dynamic model of meaning. Journal of Pragmatics 40 (3):385–406. doi:10.1016/j.pragma.2007.12.004.
   Web of Science ®Google Scholar
• Kecskes, I. 2021. Processing implicatures in English as a Lingua Franca communication. Lingua 256:103067. doi:10.1016/j.lingua.2021.103067.
   Web of Science ®Google Scholar
• Lassiter, C. 2021. Particularized conversational implicatures and miraculous communication. Language Sciences 86:101401. doi:10.1016/j.langsci.2021.101401.
   Web of Science ®Google Scholar
• Li, H. 2022. Methods of machine learning. Beijing: Tsinghua University Press.
   Google Scholar
• Li, X.-B., H.-R. Qiao, Z.-C. Ma, D.-D. Yang, Y.-T. Pan, Z.-X. Ma, and Machinery Assoc Comp. 2018. “Bayesian belief network model of indirect speech act theory.” International Conference on Artificial Intelligence and Virtual Reality (AIVR)/International Conference on Big Data and Machine Learning (BDML), Nagoya, JAPAN, Nov 23-25.
   Google Scholar
• McCarthy, M., and S. Jarvis. 2010. MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods 42 (2):381–92. doi:10.3758/brm.42.2.381.
   PubMed Web of Science ®Google Scholar
• Monte-Serrat, M., and C. Cattani. 2021. The natural language for artificial intelligence. Cambridge: Academic Press.
   Google Scholar
• Potts, C. 2005. The logic of conventional implicatures. Oxford: Oxford University Press.
   Google Scholar
• Sbisà, M. 2021. Presupposition and implicature: Varieties of implicit meaning in explicitation practices. Journal of Pragmatics 182:176–88. doi:10.1016/j.pragma.2021.05.027.
   Web of Science ®Google Scholar
• Turney, D., and P. Pantel. 2010. From frequency to meaning: Vector space models of semantics. The Journal of Artificial Intelligence Research 37:141–88. doi:10.1613/jair.2934.
   Web of Science ®Google Scholar
• Wylie, E., S. George, K. McWilliams, A. Evans, and S. Stolzenberg. 2022. Children’s acquiescence to polysemous implicature questions about coaching: The role of parental support. Journal of Applied Developmental Psychology 78:101370. doi:10.1016/j.appdev.2021.101370.
   PubMed Web of Science ®Google Scholar
• Zhou, Z.-H. 2021. Machine learning. Singapore: Springer Nature Singapore Pte Ltd.
   Google Scholar