Solving algebraic word problems using text mining

Abstract

Development of experimental techniques and computational models accelerated the pace of research and development in the eld of mathematics and text mining. One of the major research area in the eld of automation is solving word problems using Arti cial Intelligence (AI) techniques. The existing research, for solving word problems using AI, is unable to provide on the spot solutions for a given word problem. The existing approaches, for solving algebraic and arithmetic word problems, have some limitations that need to be addressed. They are limited to give high accuracy for solving arithmetic problems only. They also require too much processing resources and time for extracting and simplifying features from the text data. In this research work, we proposed a template-based approach that has been developed by following a two-step process. The rst step is to predict an equation template from a training dataset. The next step is to instantiate the predicted template with nouns and numbers. The features extraction in the proposed approach only relays on POS tags, NER and dependency relation; no need to extract all words from the given word problem. To validate the proposed methodology, a prototype system has been implemented. The system has been compared with the existing systems using their respective datasets and the proposed dataset. The experimental results show improvement in accuracy, with an average precision of 80.6% and average recall of 83.5%. In future, we intend to incorporate the co-reference resolution technique into our system to further improve its accuracy.