Modeling Math Word Problems with Augmented Semantic Networks

Christian Liguda, Thies Pfeiffer

In: Gosse Bouma , Ashwin Ittoo , Elisabeth Métais , Hans Wortmann (Hrsg.). Natural Language Processing and Information Systems. International Conference on Applications of Natural Language to Information Systems (NLDB-2012) 17th June 23-26 Groningen Netherlands Seiten 247-252 Springer 2012.


Abstract Modern computer-algebra programs are able to solve a wide range of mathematical calculations. However, they are not able to under- stand and solve math text problems in which the equation is described in terms of natural language instead of mathematical formulas. Interest- ingly, 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), 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 Ger- man primary school books and could be extended to other languages by exchanging the language model in the natural language processing module.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence