Automatic Question Generation Based on Analysis of Sentence Structure

Blšták, Miroslav; Rozinajová, Viera

doi:10.1007/978-3-319-45510-5_26

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…A detailed description of the dataset, along with the results achieved by the participants, is given in Rus et al (2012). Blšták andRozinajová (2017, 2018) used this dataset to generate questions and compare their performance on correctness to the performance of the systems participating in the shared task. -Medical CBQ corpus ) is a corpus of 435 case-based, auto-generated questions that follow four templates ("What is the most likely diagnosis?…”

Section: Standard Datasetsmentioning

confidence: 99%

A Systematic Review of Automatic Question Generation for Educational Purposes

Kurdi

Leo

Parsia

et al. 2019

Int J Artif Intell Educ

241

111

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While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience, and resources. This, in turn, hinders and slows down the use of educational activities (e.g. providing practice questions) and new advances (e.g. adaptive testing) that require a large pool of questions. To reduce the expenses associated with manual construction of questions and to satisfy the need for a continuous supply of new questions, automatic question generation (AQG) techniques were introduced. This review extends a previous review on AQG literature that has been published up to late 2014. It includes 93 papers that were between 2015 and early 2019 and tackle the automatic generation of questions for educational purposes. The aims of this review are to: provide an overview of the AQG community and its activities, summarise the current trends and advances in AQG, highlight the changes that the area has undergone in the recent years, and suggest areas for improvement and future opportunities for AQG. Similar to what was found previously, there is little focus in the current literature on generating questions of controlled difficulty, enriching question forms and structures, automating template construction, improving presentation, and generating feedback. Our findings also suggest the need to further improve experimental reporting, harmonise evaluation metrics, and investigate other evaluation methods that are more feasible.

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Section: Standard Datasetsmentioning

confidence: 99%

A Systematic Review of Automatic Question Generation for Educational Purposes

Kurdi

Leo

Parsia

et al. 2019

Int J Artif Intell Educ

241

111

View full text Add to dashboard Cite

show abstract

“…This research has not been able to make inquiries from sentences that have pronouns. Blstak & Viera [9] conduct question generation based on template based method derived from sentence structure analysis. This study yields a more effective pattern than previous studies because it has fewer patterns but has better results in generating questions.…”

Section: Introductionmentioning

confidence: 99%

Generating Indonesian Question Automatically Based on Bloom’s Taxonomy Using Template Based Method

Kusuma¹,

Alhamri²

2018

KINETIK

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In education field, evaluation is needed to know the extent to which the learning process has been done. The evaluation process can be done through the provision of questions with varying degrees of difficulty. However, making questions with varying degrees of difficulty is not easy. Someone must understand the whole new materials to make the question. If there are a lot of materials, it takes a little time to change them to be a question. Therefore, it is necessary to automate the question generation process, in order to facilitate and accelerate the question generation process. This research introduces a template-based method to generate questions based on New Bloom's Taxonomy. There were 4 stages in this research, dataset collection, pattern identification process, question generating process & classification, and final evaluation process result. The dataset consists of 60 samples of paragraphs that derived from 9 courses of study courses Informatics Engineering. The 60 paragraphs produced 278 sentences and 654 questions. The proposed method is capable of producing an accuracy of 81.65% to generate questions using New Bloom's Taxonomy classification. So it can be concluded that the proposed method can be used to generate questions with varying difficulty levels in accordance with New Bloom's Taxonomy.

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“…The second wave of QG systems inherited the classic pipeline of the rule-based QG systems while enhancing their performance with machine learning techniques that serve one or more phases of QG. These statistical models may be applied in either text preprocessing for sentence selection [29,44] and classification [45], question construction for predicting which template to apply [19], and post-generation processing for prioritising good quality question [20,[46][47][48][49]. Figure 2.9 depicts a summary of that.…”

Section: Empirical Approachesmentioning

confidence: 99%

Automatic question generation from natural language texts

Cao¹

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Automatic Question Generation Based on Analysis of Sentence Structure

Cited by 6 publications

References 5 publications

A Systematic Review of Automatic Question Generation for Educational Purposes

A Systematic Review of Automatic Question Generation for Educational Purposes

Generating Indonesian Question Automatically Based on Bloom’s Taxonomy Using Template Based Method

Automatic question generation from natural language texts

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