Solving Arithmetic Word Problems Using Natural Language Processing and Rule-Based Classification

Abstract

In today's world, intelligent tutoring systems (ITS), computer-based training (CBT), etc. are rapidly gaining popularity in both educational and professional fields, and an automatic solver for mathematical word problems is one of the most important subfields of ITS. Automatic solving of mathematical word problems is a challenging research problem in the fields of artificial intelligence (AI) and its subfields like natural language processing (NLP), machine learning (ML), etc., since understanding and extracting relevant information from an unstructured text requires a lot of logical skills. To date, much research has been done in this area, focusing on solving each type of mathematical word problem, such as arithmetic word problems, algebraic word problems, geometric word problems, trigonometric word problems, etc. In this paper, we present an approach to automatically solve arithmetic word problems. We use a rule-based approach to classify word problems. We propose various rules to establish the relationships and dependencies among different key elements and classify the word problems into four categories (Change, Combine, Compare, and Division-Multiplication) and their subcategories to identify the desired operation among+,-, *, and /. However, it is limited to solving only word problems with a single operation and a single equation word problem. Irrelevant information is also filtered out from the input problem texts, based on manually created rules to extract relevant quantities. Later, an equation is formed with the relevant quantities and the predicted operation to obtain the final answer. The work proposed here performs well compared to most similar systems based on the standard SingleOp dataset, achieving an accuracy of 93.02%.