Modeling math word problems with augmented semantic networks
Modern computer-algebra programs are able to solve a wide
range of mathematical calculations. However, they are not able to understand and solve math text problems in which the equation is described in terms of natural language instead of mathematical formulas. Interestingly, there are only few known approaches to solve math word problems algorithmically and most of employ models based on frames. To overcome problems with existing models, we propose a model based on augmented semantic networks to represent the mathematical structure behind word problems. This model is implemented in our Solver for Mathematical Text Problems (SoMaTePs) [1], where the math problem is extracted via natural language processing, transformed in mathematical equations and solved by a state-of-the-art computer-algebra program. SoMaTePs is able to understand and solve mathematical text problems from German primary school books and could be extended to other languages by exchanging the language model in the natural language processing module.
7337
247-252
247-252
Springer