Abstract
Hypergeometric distribution is one of the most important mathematical models in probabilistic subjects, and also an integral part of the intelligent tutoring system based on automatic machine solving. In this paper, we propose a set of solutions to automatically solve the Hypergeometric distribution problems. Our method combines the syntactic and semantic information of the subject, establishes the matching rule between the topic narration and the type template, and implement the problem solving by establishing the solution formula corresponding to the topic, as well as extracting and complementing the corresponding problem data. Experiments conducted on a dataset which collected from the Internet and College Entrance Examination questions over the years demonstrate the feasibility of our method.
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References
Bobrow, D.G.: Natural language input for a computer problem solving system (1964)
Chou, S.C., Gao, X.S., Zhang, J.Z.: Automated production of traditional proofs for theorems in Euclidean geometry. In: Proceedings of Eigth IEEE Symposium on Login in Computer Science, pp. 48–56 (1993)
Dellarosa, D.: A computer simulation of children’s arithmetic word-problem solving. Behav. Res. Methods 18(2), 147–154 (1986)
Hosseini, M.J., Hajishirzi, H., Etzioni, O., Kushman, N.: Learning to solve arithmetic word problems with verb categorization. In: EMNLP, pp. 523–533 (2014)
Huang, C.-Y., Ren, Q.-L., L.M.: The de-composability and representation of strategies used in auto-solving system of primary school mathematic word problems. Mod. Educ. Technol. 20, 24–27 (2010)
Jianliang, Y.: Hypergeometric distribution and its generalization. J. TaiYuan Normal Univ. 12, 20–23 (2013)
Kintsch, W.: Learning from text. Cognit. Instr. 3(2), 87–108 (1986)
Kintsch, W., Greeno, J.G.: Understanding and solving word arithmetic problems. Psychol. Rev. 92(1), 109 (1985)
Kushman, N., Artzi, Y., Zettlemoyer, L., Barzilay, R.: Learning to Automatically Solve Algebra Word Problems. Association for Computational Linguistics (2014)
Liguda, C., Pfeiffer, T.: Modeling math word problems with augmented semantic networks. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds.) NLDB 2012. LNCS, vol. 7337, pp. 247–252. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31178-9_29
Ma, Y.H., Tan, K., Shang, X.J.: Research on method of semantic comprehension based on semantic sentence template. Comput. Technol. Dev. 10, 031 (2012)
Tun, W.D.: On the decision problem and the mechanization of theorem-proving in elementary geometry. Sci. Sinica 21(2), 159–172 (1978)
Zhang, J.Z., Chou, S.C., Gao, X.S.: Automated production of traditional proofs for theorems in euclidean geometry i. The hilbert intersection point theorems. Ann. Math. Artif. Intell. 13(1), 109–137 (1995)
Zhang, J.Z., Yang, L., Yang, X.C.: The realization of elementary configurations in euclidean space. Sci. China Ser. A-Math. Phys. Astron. Technol. Sci. 37(1), 15–26 (1994)
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This work is supported by the Fundamental Research Funds for the Central Universities (No. 20205170442).
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Sun, C., Su, Y., Yu, X. (2018). Machine Solving on Hypergeometric Distribution Problems. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_9
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DOI: https://doi.org/10.1007/978-3-319-92753-4_9
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