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Estimating the difficulty level of math word problems is an important taskfor many educational applications. Identification of relevant and irrelevantsentences in math word problems is an important step for calculating thedifficulty levels of such problems. This paper addresses a novel application oftext categorization to identify two types of sentences in mathematical wordproblems, namely relevant and irrelevant sentences. A novel joint probabilisticclassification model is proposed to estimate the joint probability ofclassification decisions for all sentences of a math word problem by utilizingthe correlation among all sentences along with the correlation between thequestion sentence and other sentences, and sentence text. The proposed model iscompared with i) a SVM classifier which makes independent classificationdecisions for individual sentences by only using the sentence text and ii) anovel SVM classifier that considers the correlation between the questionsentence and other sentences along with the sentence text. An extensive set ofexperiments demonstrates the effectiveness of the joint probabilisticclassification model for identifying relevant and irrelevant sentences as wellas the novel SVM classifier that utilizes the correlation between the questionsentence and other sentences. Furthermore, empirical results and analysis showthat i) it is highly beneficial not to remove stopwords and ii) utilizing partof speech tagging does not make a significant improvement although it has beenshown to be effective for the related task of math word problem typeclassification.

A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems