• The Journal of Bigdata
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• v.6 no.1
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• pp.63-81
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• 2021

The performance of natural language processing is rapidly improving due to the recent development and application of machine learning and deep learning technologies, and as a result, the field of application is expanding. In particular, as the demand for analysis on unstructured text data increases, interest in NLP(Natural Language Processing) is also increasing. However, due to the complexity and difficulty of the natural language preprocessing process and machine learning and deep learning theories, there are still high barriers to the use of natural language processing. In this paper, for an overall understanding of NLP, by examining the main fields of NLP that are currently being actively researched and the current state of major technologies centered on machine learning and deep learning, We want to provide a foundation to understand and utilize NLP more easily. Therefore, we investigated the change of NLP in AI(artificial intelligence) through the changes of the taxonomy of AI technology. The main areas of NLP which consists of language model, text classification, text generation, document summarization, question answering and machine translation were explained with state of the art deep learning models. In addition, major deep learning models utilized in NLP were explained, and data sets and evaluation measures for performance evaluation were summarized. We hope researchers who want to utilize NLP for various purposes in their field be able to understand the overall technical status and the main technologies of NLP through this paper.

• Journal of Science Education
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• v.38 no.3
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• pp.670-690
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• 2014

The purpose of this study was to analyze paper and pencil test items of life science I in high school to diagnose problems of the test items developed by teachers, and to provide some implication for better assessment. 690 selection-type items and 162 supply-type items in life science I were collected from 10 general high schools. In the analysis of test items, the ratio of the selection-type item and the supply-type item was 81:19 in the number of items based on item type, while the ratio was 74.4:25.6 in the distribution of marks, indicating that the distribution of marks compared to the number of items was higher in the supply-type items. In the analysis by the Bloom's revised taxonomy of educational objectives, the items of 'conceptual knowledge' in the knowledge and those of 'understanding' in the cognition process were shown most in both the selection-type item and the supply-type item. In the analysis by the science assessment frameworks of NAEA, the items of 'knowledge' were shown 9 times more than those of 'inquiry'. When compared to the level of difficulty presented in the two-way specification table and the percentage of correct answers in the selection-type item, the concurrence was 41.5%. When compared to the ratio of number of items based on the item type of the supply-type items, the short-answer items were 34.0%, the descriptive items were 61.1%, and the drawing items were 4.9%. The drawing items were mainly developed in the unit of 'Cells and Continuity of Life'. When the descriptive items were classified by the acceptance of response, all the items were 'response restricted' type, and the items of 'restricted in content range' type among them were highest. When the items were classified by presentation of data, the items of 'presentation of data' type were highest(65.4%), and when classified by type of question, the items of 'knowledge description' type were highest(80.4%). In conclusion, it is needed to develop items belonging to 'inquiry' area more in the school, and to increase the ratio of the descriptive items, presenting various types of items.