Machine Solving on Hypergeometric Distribution Problems

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.