Publication
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, Pages 247-252, Springer, 2012.
Abstract
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.