• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.575-585
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• 2018

In this study, the texts of science textbooks of the past 20 years were collected in order to systematically carry out researches on scientific languages and scientific terms that have not been noticed in science education. We have collected all the science textbooks from elementary school to high school in the 6th curriculum, the 7th curriculum, and the 2009 revised curriculum, and constructed a corpus comprising of 132 textbooks in total. Sequentially, a raw corpus, a morphological annotated corpus, and a semantic annotated corpus of science terms, were constructed. The final constructed science textbook corpus was named K-STeC (Korean Science Textbook Corpus). K-STeC is a semantic annotated corpus with semantic classification and classification of scientific terms, together with meta information of bibliographic information such as curriculum, subject, grade, and publisher, location information such as chapter, section, lesson, page, and sentence, and structure information such as main, inquiry activities, reference materials, and titles. Throughout the three-year study period, a new research method was created by integrating the know-how of the three fields of linguistic informatics, computer science and science education, and a large number of experts were put in to produce labor-intensive results. This paper introduces new research methodologies and outcomes by looking at the whole research process and methods, and discusses the possibility of future development of scientific language research and how to use the results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2718-2731
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• 2023

This paper presents a method of using syntax and shallow semantic analysis for Vietnamese question generation (QG). Specifically, our proposed technique concentrates on investigating both the syntactic and shallow semantic structure of each sentence. The main goal of our method is to generate questions from a single sentence. These generated questions are known as factoid questions which require short, fact-based answers. In general, syntax-based analysis is one of the most popular approaches within the QG field, but it requires linguistic expert knowledge as well as a deep understanding of syntax rules in the Vietnamese language. It is thus considered a high-cost and inefficient solution due to the requirement of significant human effort to achieve qualified syntax rules. To deal with this problem, we collected the syntax rules in Vietnamese from a Vietnamese language textbook. Moreover, we also used different natural language processing (NLP) techniques to analyze Vietnamese shallow syntax and semantics for the QG task. These techniques include: sentence segmentation, word segmentation, part of speech, chunking, dependency parsing, and named entity recognition. We used human evaluation to assess the credibility of our model, which means we manually generated questions from the corpus, and then compared them with the generated questions. The empirical evidence demonstrates that our proposed technique has significant performance, in which the generated questions are very similar to those which are created by humans.