{"title":"A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection","authors":"Mukta Majumder, S. Saha","doi":"10.18653/v1/W15-4410","DOIUrl":null,"url":null,"abstract":"Multiple Choice Question (MCQ) plays a major role in educational assessment as well as in active learning. In this paper we present a system that generates MCQs automatically using a sports domain text as input. All the sentences in a text are not capable of generating MCQs; the first step of the system is to select the informative sentences. We propose a novel technique to select informative sentences by using topic modeling and parse structure similarity. The parse structure similarity is computed between the parse structure of an input sentence and a set of reference parse structures. In order to compile the reference set we use a number of existing MCQs collected from the web. Keyword selection is done with the help of occurrence of domain specific word and named entity word in the sentence. Distractors are generated using a set of rules and name dictionary. Experimental results demonstrate that the proposed technique is quite accurate.","PeriodicalId":316430,"journal":{"name":"NLP-TEA@ACL/IJCNLP","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NLP-TEA@ACL/IJCNLP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/W15-4410","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 34
Abstract
Multiple Choice Question (MCQ) plays a major role in educational assessment as well as in active learning. In this paper we present a system that generates MCQs automatically using a sports domain text as input. All the sentences in a text are not capable of generating MCQs; the first step of the system is to select the informative sentences. We propose a novel technique to select informative sentences by using topic modeling and parse structure similarity. The parse structure similarity is computed between the parse structure of an input sentence and a set of reference parse structures. In order to compile the reference set we use a number of existing MCQs collected from the web. Keyword selection is done with the help of occurrence of domain specific word and named entity word in the sentence. Distractors are generated using a set of rules and name dictionary. Experimental results demonstrate that the proposed technique is quite accurate.