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ERIC Number: EJ1115359
Record Type: Journal
Publication Date: 2010
Pages: 19
Abstractor: As Provided
ISSN: EISSN-2157-2100
A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems
Cetintas, Suleyman; Si, Luo; Xin, Yan Ping; Zhang, Dake; Park, Joo Young; Tzur, Ron
Journal of Educational Data Mining, v2 n1 p83-101 2010
Estimating the difficulty level of math word problems is an important task for many educational applications. Identification of relevant and irrelevant sentences in math word problems is an important step for calculating the difficulty levels of such problems. This paper addresses a novel application of text categorization to identify two types of sentences in "mathematical word problems", namely "relevant" and "irrelevant" sentences. A novel joint probabilistic classification model is proposed to estimate the joint probability of classification decisions for all sentences of a math word problem by utilizing the correlation among all sentences along with the correlation between the question sentence and other sentences, and sentence text. The proposed model is compared with (i) a SVM classifier which makes independent classification decisions for individual sentences by only using the sentence text, and (ii) a novel SVM classifier that considers the correlation between the question sentence and other sentences along with the sentence text. An extensive set of experiments demonstrates the effectiveness of the joint probabilistic classification model for identifying relevant and irrelevant sentences, as well as the novel SVM classifier that utilizes the correlation between the question sentence and other sentences. Furthermore, empirical results and analysis show that (i) it is highly beneficial not to remove stopwords, and (ii) utilizing part of speech tagging does not make a significant improvement although it has been shown to be effective for the related task of math word problem type classification.
International Educational Data Mining. e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: IIS0749462; IIS0746830; DRL0822296