Solving Sinhala Language Arithmetic Problems using Neural Networks

TL;DR

This paper introduces a neural network-based system for solving Sinhala language arithmetic problems by combining keyword, question, and operation identification with classification techniques, achieving notable accuracy improvements.

Contribution

It presents a novel neural network approach integrating multiple classification methods for Sinhala arithmetic problem solving, outperforming existing systems.

Findings

Mahoshadha2 achieves 76% accuracy.

The combined neural network approach improves problem-solving performance.

Comparison shows advantages over previous methods.

Abstract

A methodology is presented to solve Arithmetic problems in Sinhala Language using a Neural Network. The system comprises of (a) keyword identification, (b) question identification, (c) mathematical operation identification and is combined using a neural network. Naive Bayes Classification is used in order to identify keywords and Conditional Random Field to identify the question and the operation which should be performed on the identified keywords to achieve the expected result. "One vs. all Classification" is done using a neural network for sentences. All functions are combined through the neural network which builds an equation to solve the problem. The paper compares each methodology in ARIS and Mahoshadha to the method presented in the paper. Mahoshadha2 learns to solve arithmetic problems with the accuracy of 76%.