Shaohui Chen
ABSTRACT
In automatic solution for mathematical problem by machines, problem understanding is an indispensable prerequisite to answer correctly. In the current methods, problem understanding refers to obtaining equivalent representations of the problems, such as linear equations, polynomials, and so on. However, in the solution process, the problem equivalent representation does not guarantee semantic interoperability with the problem, and it is not easy to generate a human-like solution by backtracking the solution process. Therefore, creating a clear equivalent representation of the question is the direction of the automatic solution. Based on the WordEmbedding technology, this paper proposes a new method to convert the math problems of natural language descriptions into equivalent semantic vector representations that are easy to understand by computers, and to identify the types of problems based on deep learning. In this paper, we take the famous Chinese ancient math problem of "chicken and rabbit in the same cage" as an example, and obtain a good recognition rate of 72%.
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Index Terms
- A Novel Approach for Understanding Primary Mathematical Problems Described in Natural Language
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