Solving Sinhala Language Arithmetic Problems using Neural Networks (original) (raw)

Deep Neural Network based system for solving Arithmetic Word problems

2017

This paper presents DILTON a system which solves simple arithmetic word problems. DILTON uses a Deep Neural based model to solve math word problems. DILTON divides the question into two parts - worldstate and query. The worldstate and the query are processed separately in two different networks and finally, the networks are merged to predict the final operation. We report the first deep learning approach for the prediction of operation between two numbers. DILTON learns to predict operations with 88.81% accuracy in a corpus of primary school questions.

Mathematical word problem categorization using Machine Learning

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2017

Text categorization is a fundamental processing of Natural Language task. Mathematical word problem has a wide variety of classes to categorize, for the further processing of word problems. The classification of word problems plays an important role for text categorization, prediction of algebraic equation for a particular problem in the segment. Word problem classification will be a step towards word problem solving automatically. In this paper comparison of machine learning classifiers such as SVM, Decision Tree, k-Nearest Neighbour, Neural Network and Convolutional Neural Network are used for classification of four types of mathematical problems taking from elementary grade level. Addition, Subtraction, Multiplication and Division problems are the categories chosen from class 3 and class 4 mathematics workbook.

Mathematical Analysis of Problem Statements: Artificial Intelligence

The calculation of a desired mathematical value is just an arithmetic function with inputs and their respective outputs. Apart from the numbers as inputs and calculating value, the analysis of a language presenting the problem of mathematics can add towards the further advancement of an automated function. This paper discusses the methods to learn and analyze simpler problems of mathematics in algebra and commerce in order to select the desired formula and extraction of the variables as the input parameters.

A Novel Framework for Math Word Problem Solving

International Journal of Information and Education Technology, 2013

Mathematical word problems represent many real world scenarios. Often timesfound difficult to solve. The reason for their difficulty is because of miscomprehension and the inability to formulate a mathematical representation of the text. In this paper we present a framework based on fuzzy logicontology model that interprets a mathematical word problem in natural language and compute a solution. This framework uses ontology as a working memory and search engine to compute the solution. Fuzzy logic is used to determine the confidence of result returned, which could eliminate the confusion for a user trying to determine if the solution provided is correct. The ability to interpret a mathematical word problem and return a detailed solution will help educated users by providing them detailed steps of a solution.

Arabic Arithmetic Word Problems Solver

Procedia Computer Science, 2017

The recent evolution in Natural Language Processing (NLP) and machine learning have played a crucial role in the development of solving word problems written in human language. This paper, to the best of our knowledge, presents the first attempt of automatically solving Arabic arithmetic word problems. In addition, as part of this work, we prepared an Arabic annotated dataset by translating a standard arithmetic word problems English dataset (AddSub Dataset). The AddSub dataset has been used by several researchers to evaluate their models for English arithmetic word problems. The proposed algorithm relies on our automatic verbs learning approach based on the training dataset. Moreover, the algorithm utilizes various NLP tools to assign objects to problem states until reaching to the goal state such as Stanford Parser, Named Entity Recognition (NER), and Cosine Metric Distance. Our approach overcomes various issues such as tracking both entities and their related results during the transfer process as well as dealing with different forms of the same verb. The performance evaluation process showed promising results resolving 80.78% of the problems. On the other hand, there are still several areas that can be extended and improved. For instance, the lack of common knowledge, presence of irrelevant information, and quantity conversions.

Solving arithmetic problems using feed-forward neural networks

Neurocomputing, 1998

We design new feed-forward multi-layered neural networks which perform di erent elementary arithmetic operations, such as bit shifting, addition of N p -bit numbers, and multiplication of two n-bit numbers. All the structures are optimal in depth and are polinomialy bounded in the number of neurons and in the number of synapses. The whole set of synaptic couplings and thresholds are obtained exactly.

Arithmetic Word Problem Solver using Unit Dependency Graph and Verb Categorization

Nowadays, the arithmetic questions that are expressed in natural language such as English are hugely getting interest by researchers. Although some useful researches have been proposed to solve word problems, there are still gaps in implementing a robust arithmetic word problem solver as the answers of the word problems cannot be easily extracted with the approach of keyword or pattern matching. According to this motivation, this research focuses on generating the correct equation from the word problem and deriving the solution. The aim of this proposed work is to implement an arithmetic word problem solver that can understand the elementary math word problems, derive the symbolic equation, and generate the result from the equation. The system is implemented with the combination of the verb semantics and the graph. The elementary student can obtain many benefits since the system is resulted the equation along with the answers. Keywords-Mathematical Word Problem (MWP), Natural Language, Arithmetic Word Problem, Word Problem Solver.

Automatic Equation Solver - A Technology Enhancement For Instructional Automation

SSRN Electronic Journal, 2019

Mathematics plays an important role in everybody's life. We mainly depend on calculators available in our electronic gadgets to solve mathematical equations. Calculators available in these gadgets are so advanced that it can solve most complex mathematical equations. Remembering the fact that almost all electronic gadgets are touch screen based now-a-days, developing a system that recognizes and solves mathematical equations from handwriting is a potential area of research. This work develops an automatic equation solver, which solves the mathematical equations written by different persons. Neural network approach is used for recognition of the symbols and alphabets. The system is able to recognise alphabets, all the numbers, mathematical symbols and some special symbols. The system is successful in solving polynomial equations of power up to degree 9, system of linear equations with 2 and 3 variables having finite, infinite and no solutions. The system gives a recognition accuracy of 90% out of the 60 equations tested.

Neural Computing based Part of Speech for Arabic Language.pdf

2019

This work proves that the using of ANN in utilizing the POS is achieving very well results. The performance has based the rate of accuracy, which most of the proposed models were obtained high accuracy between 90% and 99%. Besides, the using of neural models required less number of tag-sets for training and testing of the model. Most of NLP applications required accurate and fast POS, which is offered by the neural model