Learning to Solve Arithmetic Word Problems with Verb Categorization

Mohammad Javad Hosseini; Hannaneh Hajishirzi; Oren Etzioni; Nate Kushman

doi:10.3115/v1/d14-1058

Abstract

This paper presents a novel approach to learning to solve simple arithmetic word problems.Our system, ARIS, analyzes each of the sentences in the problem statement to identify the relevant variables and their values.ARIS then maps this information into an equation that represents the problem, and enables its (trivial) solution as shown in Figure 1.The paper analyzes the arithmetic-word problems "genre", identifying seven categories of verbs used in such problems.ARIS learns to categorize verbs with 81.2% accuracy, and is able to solve 77.7% of the problems in a corpus of standard primary school test questions.We report the first learning results on this task without reliance on predefined templates and make our data publicly available.

Keywords

CategorizationVerbComputer scienceNatural language processingArithmeticArtificial intelligenceWord (group theory)LinguisticsMathematics

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Publication Info

Year: 2014
Type: article
Citations: 284
Access: Closed

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APA Style

                            
                                    Mohammad Javad Hosseini, 
                                
                                    Hannaneh Hajishirzi, 
                                
                                    Oren Etzioni
                                
                                et al.
                            
                            (2014). 
                            Learning to Solve Arithmetic Word Problems with Verb Categorization. 
                            Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
                            
                            .
                            https://doi.org/10.3115/v1/d14-1058

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DOI: 10.3115/v1/d14-1058

Data Quality

Data completeness: 81%