• Title/Summary/Keyword: 언어 모델링

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A Construction of the C_MDR(Component_MetaData Registry) for the Environment of Exchanging the Component (컴포넌트 유통환경을 위한 컴포넌트 메타데이타 레지스트리 구축 : C_MDR)

  • Song, Chee-Yang;Yim, Sung-Bin;Baik, Doo-Kwon;Kim, Chul-Hong
    • Journal of KIISE:Computing Practices and Letters
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    • v.7 no.6
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    • pp.614-629
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    • 2001
  • As the information-intensive society in 21c based on the environment of global internet is promoted, the software is getting more large and complex, and the demand for the software is increasing briskly. So, it becomes an important issue in academic and industrial field to activate reuse by developing and exchanging the standardized component. Currently, the information services as a product type of each company are provided in foreign market place for reusing a commercial component, but the components which are serviced in each market place are different, insufficient and unstandardized. That is, construction for Component Data Registry based on ISO 11179, is not accomplished. Hence, the national government has stepped up the plan for sending out public component at 2001. Therefore, the systems as a tool for sharing and exchange of data, have to support the meta-information of standardized component. In this paper, we will propose the C_MDR system: a tool to register and manage the standardized meta-information, based upon ISO 11179, for the commercialized common component. The purpose of this system is to systemically share and exchange the data in chain of acceleration of reusing the component. So, we will show the platform of specification for the component meta-information, then define the meta-information according to this platform, also represent the meta-information using XML for enhancing the interoperability of information with other system. Moreover, we will show that three-layered expression make modeling to be simple and understandable. The implementation of this system is to construct a prototype system of the component meta-information through the internet on www, this system uses ASP as a development language and RDBMS Oracle for PC. Thus, we may expect the standardization of the exchanged component metadata, and be able to apply to the exchanged reuse tool.

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Exploring Opinions on University Online Classes During the COVID-19 Pandemic Through Twitter Opinion Mining (트위터 오피니언 마이닝을 통한 코로나19 기간 대학 비대면 수업에 대한 의견 고찰)

  • Kim, Donghun;Jiang, Ting;Zhu, Yongjun
    • Journal of the Korean Society for Library and Information Science
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    • v.55 no.4
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    • pp.5-22
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    • 2021
  • This study aimed to understand how people perceive the transition from offline to online classes at universities during the COVID-19 pandemic. To achieve the goal, we collected tweets related to online classes on Twitter and performed sentiment and time series topic analysis. We have the following findings. First, through the sentiment analysis, we found that there were more negative than positive opinions overall, but negative opinions had gradually decreased over time. Through exploring the monthly distribution of sentiment scores of tweets, we found that sentiment scores during the semesters were more widespread than the ones during the vacations. Therefore, more diverse emotions and opinions were showed during the semesters. Second, through time series topic analysis, we identified five main topics of positive tweets that include class environment and equipment, positive emotions, places of taking online classes, language class, and tests and assignments. The four main topics of negative tweets include time (class & break time), tests and assignments, negative emotions, and class environment and equipment. In addition, we examined the trends of public opinions on online classes by investigating the changes in topic composition over time through checking the proportions of representative keywords in each topic. Different from the existing studies of understanding public opinions on online classes, this study attempted to understand the overall opinions from tweet data using sentiment and time series topic analysis. The results of the study can be used to improve the quality of online classes in universities and help universities and instructors to design and offer better online classes.