25 research outputs found

    ํ•œ๊ตญ์–ด ์‚ฌ์ „ํ•™์Šต๋ชจ๋ธ ๊ตฌ์ถ•๊ณผ ํ™•์žฅ ์—ฐ๊ตฌ: ๊ฐ์ •๋ถ„์„์„ ์ค‘์‹ฌ์œผ๋กœ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ธ๋ฌธ๋Œ€ํ•™ ์–ธ์–ดํ•™๊ณผ, 2021. 2. ์‹ ํšจํ•„.Recently, as interest in the Bidirectional Encoder Representations from Transformers (BERT) model has increased, many studies have also been actively conducted in Natural Language Processing based on the model. Such sentence-level contextualized embedding models are generally known to capture and model lexical, syntactic, and semantic information in sentences during training. Therefore, such models, including ELMo, GPT, and BERT, function as a universal model that can impressively perform a wide range of NLP tasks. This study proposes a monolingual BERT model trained based on Korean texts. The first released BERT model that can handle the Korean language was Google Researchโ€™s multilingual BERT (M-BERT), which was constructed with training data and a vocabulary composed of 104 languages, including Korean and English, and can handle the text of any language contained in the single model. However, despite the advantages of multilingualism, this model does not fully reflect each languageโ€™s characteristics, so that its text processing performance in each language is lower than that of a monolingual model. While mitigating those shortcomings, we built monolingual models using the training data and a vocabulary organized to better capture Korean textsโ€™ linguistic knowledge. Therefore, in this study, a model named KR-BERT was built using training data composed of Korean Wikipedia text and news articles, and was released through GitHub so that it could be used for processing Korean texts. Additionally, we trained a KR-BERT-MEDIUM model based on expanded data by adding comments and legal texts to the training data of KR-BERT. Each model used a list of tokens composed mainly of Hangul characters as its vocabulary, organized using WordPiece algorithms based on the corresponding training data. These models reported competent performances in various Korean NLP tasks such as Named Entity Recognition, Question Answering, Semantic Textual Similarity, and Sentiment Analysis. In addition, we added sentiment features to the BERT model to specialize it to better function in sentiment analysis. We constructed a sentiment-combined model including sentiment features, where the features consist of polarity and intensity values assigned to each token in the training data corresponding to that of Korean Sentiment Analysis Corpus (KOSAC). The sentiment features assigned to each token compose polarity and intensity embeddings and are infused to the basic BERT input embeddings. The sentiment-combined model is constructed by training the BERT model with these embeddings. We trained a model named KR-BERT-KOSAC that contains sentiment features while maintaining the same training data, vocabulary, and model configurations as KR-BERT and distributed it through GitHub. Then we analyzed the effects of using sentiment features in comparison to KR-BERT by observing their performance in language modeling during the training process and sentiment analysis tasks. Additionally, we determined how much each of the polarity and intensity features contributes to improving the model performance by separately organizing a model that utilizes each of the features, respectively. We obtained some increase in language modeling and sentiment analysis performances by using both the sentiment features, compared to other models with different feature composition. Here, we included the problems of binary positivity classification of movie reviews and hate speech detection on offensive comments as the sentiment analysis tasks. On the other hand, training these embedding models requires a lot of training time and hardware resources. Therefore, this study proposes a simple model fusing method that requires relatively little time. We trained a smaller-scaled sentiment-combined model consisting of a smaller number of encoder layers and attention heads and smaller hidden sizes for a few steps, combining it with an existing pre-trained BERT model. Since those pre-trained models are expected to function universally to handle various NLP problems based on good language modeling, this combination will allow two models with different advantages to interact and have better text processing capabilities. In this study, experiments on sentiment analysis problems have confirmed that combining the two models is efficient in training time and usage of hardware resources, while it can produce more accurate predictions than single models that do not include sentiment features.์ตœ๊ทผ ํŠธ๋žœ์Šคํฌ๋จธ ์–‘๋ฐฉํ–ฅ ์ธ์ฝ”๋” ํ‘œํ˜„ (Bidirectional Encoder Representations from Transformers, BERT) ๋ชจ๋ธ์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ๋†’์•„์ง€๋ฉด์„œ ์ž์—ฐ์–ด์ฒ˜๋ฆฌ ๋ถ„์•ผ์—์„œ ์ด์— ๊ธฐ๋ฐ˜ํ•œ ์—ฐ๊ตฌ ์—ญ์‹œ ํ™œ๋ฐœํžˆ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์žฅ ๋‹จ์œ„์˜ ์ž„๋ฒ ๋”ฉ์„ ์œ„ํ•œ ๋ชจ๋ธ๋“ค์€ ๋ณดํ†ต ํ•™์Šต ๊ณผ์ •์—์„œ ๋ฌธ์žฅ ๋‚ด ์–ดํœ˜, ํ†ต์‚ฌ, ์˜๋ฏธ ์ •๋ณด๋ฅผ ํฌ์ฐฉํ•˜์—ฌ ๋ชจ๋ธ๋งํ•œ๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ELMo, GPT, BERT ๋“ฑ์€ ๊ทธ ์ž์ฒด๊ฐ€ ๋‹ค์–‘ํ•œ ์ž์—ฐ์–ด์ฒ˜๋ฆฌ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ๋ณดํŽธ์ ์ธ ๋ชจ๋ธ๋กœ์„œ ๊ธฐ๋Šฅํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ์–ด ์ž๋ฃŒ๋กœ ํ•™์Šตํ•œ ๋‹จ์ผ ์–ธ์–ด BERT ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ๊ฐ€์žฅ ๋จผ์ € ๊ณต๊ฐœ๋œ ํ•œ๊ตญ์–ด๋ฅผ ๋‹ค๋ฃฐ ์ˆ˜ ์žˆ๋Š” BERT ๋ชจ๋ธ์€ Google Research์˜ multilingual BERT (M-BERT)์˜€๋‹ค. ์ด๋Š” ํ•œ๊ตญ์–ด์™€ ์˜์–ด๋ฅผ ํฌํ•จํ•˜์—ฌ 104๊ฐœ ์–ธ์–ด๋กœ ๊ตฌ์„ฑ๋œ ํ•™์Šต ๋ฐ์ดํ„ฐ์™€ ์–ดํœ˜ ๋ชฉ๋ก์„ ๊ฐ€์ง€๊ณ  ํ•™์Šตํ•œ ๋ชจ๋ธ์ด๋ฉฐ, ๋ชจ๋ธ ํ•˜๋‚˜๋กœ ํฌํ•จ๋œ ๋ชจ๋“  ์–ธ์–ด์˜ ํ…์ŠคํŠธ๋ฅผ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Š” ๊ทธ ๋‹ค์ค‘์–ธ์–ด์„ฑ์ด ๊ฐ–๋Š” ์žฅ์ ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๊ฐ ์–ธ์–ด์˜ ํŠน์„ฑ์„ ์ถฉ๋ถ„ํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•˜์—ฌ ๋‹จ์ผ ์–ธ์–ด ๋ชจ๋ธ๋ณด๋‹ค ๊ฐ ์–ธ์–ด์˜ ํ…์ŠคํŠธ ์ฒ˜๋ฆฌ ์„ฑ๋Šฅ์ด ๋‚ฎ๋‹ค๋Š” ๋‹จ์ ์„ ๋ณด์ธ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ทธ๋Ÿฌํ•œ ๋‹จ์ ๋“ค์„ ์™„ํ™”ํ•˜๋ฉด์„œ ํ…์ŠคํŠธ์— ํฌํ•จ๋˜์–ด ์žˆ๋Š” ์–ธ์–ด ์ •๋ณด๋ฅผ ๋ณด๋‹ค ์ž˜ ํฌ์ฐฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ตฌ์„ฑ๋œ ๋ฐ์ดํ„ฐ์™€ ์–ดํœ˜ ๋ชฉ๋ก์„ ์ด์šฉํ•˜์—ฌ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•œ๊ตญ์–ด Wikipedia ํ…์ŠคํŠธ์™€ ๋‰ด์Šค ๊ธฐ์‚ฌ๋กœ ๊ตฌ์„ฑ๋œ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ KR-BERT ๋ชจ๋ธ์„ ๊ตฌํ˜„ํ•˜๊ณ , ์ด๋ฅผ GitHub์„ ํ†ตํ•ด ๊ณต๊ฐœํ•˜์—ฌ ํ•œ๊ตญ์–ด ์ •๋ณด์ฒ˜๋ฆฌ๋ฅผ ์œ„ํ•ด ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋˜ํ•œ ํ•ด๋‹น ํ•™์Šต ๋ฐ์ดํ„ฐ์— ๋Œ“๊ธ€ ๋ฐ์ดํ„ฐ์™€ ๋ฒ•์กฐ๋ฌธ๊ณผ ํŒ๊ฒฐ๋ฌธ์„ ๋ง๋ถ™์—ฌ ํ™•์žฅํ•œ ํ…์ŠคํŠธ์— ๊ธฐ๋ฐ˜ํ•ด์„œ ๋‹ค์‹œ KR-BERT-MEDIUM ๋ชจ๋ธ์„ ํ•™์Šตํ•˜์˜€๋‹ค. ์ด ๋ชจ๋ธ์€ ํ•ด๋‹น ํ•™์Šต ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ WordPiece ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•ด ๊ตฌ์„ฑํ•œ ํ•œ๊ธ€ ์ค‘์‹ฌ์˜ ํ† ํฐ ๋ชฉ๋ก์„ ์‚ฌ์ „์œผ๋กœ ์ด์šฉํ•˜์˜€๋‹ค. ์ด๋“ค ๋ชจ๋ธ์€ ๊ฐœ์ฒด๋ช… ์ธ์‹, ์งˆ์˜์‘๋‹ต, ๋ฌธ์žฅ ์œ ์‚ฌ๋„ ํŒ๋‹จ, ๊ฐ์ • ๋ถ„์„ ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ํ•œ๊ตญ์–ด ์ž์—ฐ์–ด์ฒ˜๋ฆฌ ๋ฌธ์ œ์— ์ ์šฉ๋˜์–ด ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด๊ณ ํ–ˆ๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” BERT ๋ชจ๋ธ์— ๊ฐ์ • ์ž์งˆ์„ ์ถ”๊ฐ€ํ•˜์—ฌ ๊ทธ๊ฒƒ์ด ๊ฐ์ • ๋ถ„์„์— ํŠนํ™”๋œ ๋ชจ๋ธ๋กœ์„œ ํ™•์žฅ๋œ ๊ธฐ๋Šฅ์„ ํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๊ฐ์ • ์ž์งˆ์„ ํฌํ•จํ•˜์—ฌ ๋ณ„๋„์˜ ์ž„๋ฒ ๋”ฉ ๋ชจ๋ธ์„ ํ•™์Šต์‹œ์ผฐ๋Š”๋ฐ, ์ด๋•Œ ๊ฐ์ • ์ž์งˆ์€ ๋ฌธ์žฅ ๋‚ด์˜ ๊ฐ ํ† ํฐ์— ํ•œ๊ตญ์–ด ๊ฐ์ • ๋ถ„์„ ์ฝ”ํผ์Šค (KOSAC)์— ๋Œ€์‘ํ•˜๋Š” ๊ฐ์ • ๊ทน์„ฑ(polarity)๊ณผ ๊ฐ•๋„(intensity) ๊ฐ’์„ ๋ถ€์—ฌํ•œ ๊ฒƒ์ด๋‹ค. ๊ฐ ํ† ํฐ์— ๋ถ€์—ฌ๋œ ์ž์งˆ์€ ๊ทธ ์ž์ฒด๋กœ ๊ทน์„ฑ ์ž„๋ฒ ๋”ฉ๊ณผ ๊ฐ•๋„ ์ž„๋ฒ ๋”ฉ์„ ๊ตฌ์„ฑํ•˜๊ณ , BERT๊ฐ€ ๊ธฐ๋ณธ์œผ๋กœ ํ•˜๋Š” ํ† ํฐ ์ž„๋ฒ ๋”ฉ์— ๋”ํ•ด์ง„๋‹ค. ์ด๋ ‡๊ฒŒ ๋งŒ๋“ค์–ด์ง„ ์ž„๋ฒ ๋”ฉ์„ ํ•™์Šตํ•œ ๊ฒƒ์ด ๊ฐ์ • ์ž์งˆ ๋ชจ๋ธ(sentiment-combined model)์ด ๋œ๋‹ค. KR-BERT์™€ ๊ฐ™์€ ํ•™์Šต ๋ฐ์ดํ„ฐ์™€ ๋ชจ๋ธ ๊ตฌ์„ฑ์„ ์œ ์ง€ํ•˜๋ฉด์„œ ๊ฐ์ • ์ž์งˆ์„ ๊ฒฐํ•ฉํ•œ ๋ชจ๋ธ์ธ KR-BERT-KOSAC๋ฅผ ๊ตฌํ˜„ํ•˜๊ณ , ์ด๋ฅผ GitHub์„ ํ†ตํ•ด ๋ฐฐํฌํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ทธ๋กœ๋ถ€ํ„ฐ ํ•™์Šต ๊ณผ์ • ๋‚ด ์–ธ์–ด ๋ชจ๋ธ๋ง๊ณผ ๊ฐ์ • ๋ถ„์„ ๊ณผ์ œ์—์„œ์˜ ์„ฑ๋Šฅ์„ ์–ป์€ ๋’ค KR-BERT์™€ ๋น„๊ตํ•˜์—ฌ ๊ฐ์ • ์ž์งˆ ์ถ”๊ฐ€์˜ ํšจ๊ณผ๋ฅผ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋˜ํ•œ ๊ฐ์ • ์ž์งˆ ์ค‘ ๊ทน์„ฑ๊ณผ ๊ฐ•๋„ ๊ฐ’์„ ๊ฐ๊ฐ ์ ์šฉํ•œ ๋ชจ๋ธ์„ ๋ณ„๋„ ๊ตฌ์„ฑํ•˜์—ฌ ๊ฐ ์ž์งˆ์ด ๋ชจ๋ธ ์„ฑ๋Šฅ ํ–ฅ์ƒ์— ์–ผ๋งˆ๋‚˜ ๊ธฐ์—ฌํ•˜๋Š”์ง€๋„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋‘ ๊ฐ€์ง€ ๊ฐ์ • ์ž์งˆ์„ ๋ชจ๋‘ ์ถ”๊ฐ€ํ•œ ๊ฒฝ์šฐ์—, ๊ทธ๋ ‡์ง€ ์•Š์€ ๋‹ค๋ฅธ ๋ชจ๋ธ๋“ค์— ๋น„ํ•˜์—ฌ ์–ธ์–ด ๋ชจ๋ธ๋ง์ด๋‚˜ ๊ฐ์ • ๋ถ„์„ ๋ฌธ์ œ์—์„œ ์„ฑ๋Šฅ์ด ์–ด๋Š ์ •๋„ ํ–ฅ์ƒ๋˜๋Š” ๊ฒƒ์„ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋•Œ ๊ฐ์ • ๋ถ„์„ ๋ฌธ์ œ๋กœ๋Š” ์˜ํ™”ํ‰์˜ ๊ธ๋ถ€์ • ์—ฌ๋ถ€ ๋ถ„๋ฅ˜์™€ ๋Œ“๊ธ€์˜ ์•…ํ”Œ ์—ฌ๋ถ€ ๋ถ„๋ฅ˜๋ฅผ ํฌํ•จํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์œ„์™€ ๊ฐ™์€ ์ž„๋ฒ ๋”ฉ ๋ชจ๋ธ์„ ์‚ฌ์ „ํ•™์Šตํ•˜๋Š” ๊ฒƒ์€ ๋งŽ์€ ์‹œ๊ฐ„๊ณผ ํ•˜๋“œ์›จ์–ด ๋“ฑ์˜ ์ž์›์„ ์š”๊ตฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„๊ต์  ์ ์€ ์‹œ๊ฐ„๊ณผ ์ž์›์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฐ„๋‹จํ•œ ๋ชจ๋ธ ๊ฒฐํ•ฉ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ ์€ ์ˆ˜์˜ ์ธ์ฝ”๋” ๋ ˆ์ด์–ด, ์–ดํ…์…˜ ํ—ค๋“œ, ์ ์€ ์ž„๋ฒ ๋”ฉ ์ฐจ์› ์ˆ˜๋กœ ๊ตฌ์„ฑํ•œ ๊ฐ์ • ์ž์งˆ ๋ชจ๋ธ์„ ์ ์€ ์Šคํ… ์ˆ˜๊นŒ์ง€๋งŒ ํ•™์Šตํ•˜๊ณ , ์ด๋ฅผ ๊ธฐ์กด์— ํฐ ๊ทœ๋ชจ๋กœ ์‚ฌ์ „ํ•™์Šต๋˜์–ด ์žˆ๋Š” ์ž„๋ฒ ๋”ฉ ๋ชจ๋ธ๊ณผ ๊ฒฐํ•ฉํ•œ๋‹ค. ๊ธฐ์กด์˜ ์‚ฌ์ „ํ•™์Šต๋ชจ๋ธ์—๋Š” ์ถฉ๋ถ„ํ•œ ์–ธ์–ด ๋ชจ๋ธ๋ง์„ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ์–ธ์–ด ์ฒ˜๋ฆฌ ๋ฌธ์ œ๋ฅผ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ๋Š” ๋ณดํŽธ์ ์ธ ๊ธฐ๋Šฅ์ด ๊ธฐ๋Œ€๋˜๋ฏ€๋กœ, ์ด๋Ÿฌํ•œ ๊ฒฐํ•ฉ์€ ์„œ๋กœ ๋‹ค๋ฅธ ์žฅ์ ์„ ๊ฐ–๋Š” ๋‘ ๋ชจ๋ธ์ด ์ƒํ˜ธ์ž‘์šฉํ•˜์—ฌ ๋” ์šฐ์ˆ˜ํ•œ ์ž์—ฐ์–ด์ฒ˜๋ฆฌ ๋Šฅ๋ ฅ์„ ๊ฐ–๋„๋ก ํ•  ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ฐ์ • ๋ถ„์„ ๋ฌธ์ œ๋“ค์— ๋Œ€ํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ๋‘ ๊ฐ€์ง€ ๋ชจ๋ธ์˜ ๊ฒฐํ•ฉ์ด ํ•™์Šต ์‹œ๊ฐ„์— ์žˆ์–ด ํšจ์œจ์ ์ด๋ฉด์„œ๋„, ๊ฐ์ • ์ž์งˆ์„ ๋”ํ•˜์ง€ ์•Š์€ ๋ชจ๋ธ๋ณด๋‹ค ๋” ์ •ํ™•ํ•œ ์˜ˆ์ธก์„ ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค.1 Introduction 1 1.1 Objectives 3 1.2 Contribution 9 1.3 Dissertation Structure 10 2 Related Work 13 2.1 Language Modeling and the Attention Mechanism 13 2.2 BERT-based Models 16 2.2.1 BERT and Variation Models 16 2.2.2 Korean-Specific BERT Models 19 2.2.3 Task-Specific BERT Models 22 2.3 Sentiment Analysis 24 2.4 Chapter Summary 30 3 BERT Architecture and Evaluations 33 3.1 Bidirectional Encoder Representations from Transformers (BERT) 33 3.1.1 Transformers and the Multi-Head Self-Attention Mechanism 34 3.1.2 Tokenization and Embeddings of BERT 39 3.1.3 Training and Fine-Tuning BERT 42 3.2 Evaluation of BERT 47 3.2.1 NLP Tasks 47 3.2.2 Metrics 50 3.3 Chapter Summary 52 4 Pre-Training of Korean BERT-based Model 55 4.1 The Need for a Korean Monolingual Model 55 4.2 Pre-Training Korean-specific BERT Model 58 4.3 Chapter Summary 70 5 Performances of Korean-Specific BERT Models 71 5.1 Task Datasets 71 5.1.1 Named Entity Recognition 71 5.1.2 Question Answering 73 5.1.3 Natural Language Inference 74 5.1.4 Semantic Textual Similarity 78 5.1.5 Sentiment Analysis 80 5.2 Experiments 81 5.2.1 Experiment Details 81 5.2.2 Task Results 83 5.3 Chapter Summary 89 6 An Extended Study to Sentiment Analysis 91 6.1 Sentiment Features 91 6.1.1 Sources of Sentiment Features 91 6.1.2 Assigning Prior Sentiment Values 94 6.2 Composition of Sentiment Embeddings 103 6.3 Training the Sentiment-Combined Model 109 6.4 Effect of Sentiment Features 113 6.5 Chapter Summary 121 7 Combining Two BERT Models 123 7.1 External Fusing Method 123 7.2 Experiments and Results 130 7.3 Chapter Summary 135 8 Conclusion 137 8.1 Summary of Contribution and Results 138 8.1.1 Construction of Korean Pre-trained BERT Models 138 8.1.2 Construction of a Sentiment-Combined Model 138 8.1.3 External Fusing of Two Pre-Trained Models to Gain Performance and Cost Advantages 139 8.2 Future Directions and Open Problems 140 8.2.1 More Training of KR-BERT-MEDIUM for Convergence of Performance 140 8.2.2 Observation of Changes Depending on the Domain of Training Data 141 8.2.3 Overlap of Sentiment Features with Linguistic Knowledge that BERT Learns 142 8.2.4 The Specific Process of Sentiment Features Helping the Language Modeling of BERT is Unknown 143 Bibliography 145 Appendices 157 A. Python Sources 157 A.1 Construction of Polarity and Intensity Embeddings 157 A.2 External Fusing of Different Pre-Trained Models 158 B. Examples of Experiment Outputs 162 C. Model Releases through GitHub 165Docto

    ์•ˆ์‚ฐ ์„์ˆ˜๊ณจ ๋งˆ์„์„ ์ค‘์‹ฌ์œผ๋กœ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ์กฐ๊ฒฝํ•™๊ณผ,2019. 8. ์„ฑ์ข…์ƒ.Urban regeneration in the past has been transformed into a resident -led improvement project in which residents themselves solve problems of daily living environment and improve them if the main goal was to regenerate the space. This was driven by changes in residents' perceptions and the formation and recovery of community relations that had been dismantled and sought a sustainable way in the long run. Among them, the garden was proposed as a solution for urban regeneration. In this study, the case of Ansan Secksugol Village which introduced the village garden as a part of the urban regeneration project where the physical characteristics of the city is poor, It is a town that was built with the growth of Ansan City and is the place where the shape of the industrial city is located. Because of the high-density plan, the multi-generational and multi-complex buildings of less than 5 stories are concentrated, and most of the old poor residential areas have poor conditions. In addition, most of the residents in the area were migrated from nearby cities and provinces. In 2007, four village gardens were constructed, and over ten years later, 38 village gardens were established. There are 24 private gardens in the private land and 4 public gardens in the public facilities. There are 10 mini gardens that utilize the squatting space. There are 24 village gardens 17 gardens, 4 public gardens, and 3 mini gardens). The purpose of this study was to investigate the meaning of spatial transformation and residents' consciousness in the low - For this purpose, the purpose of this study was to clarify why the gardens were formed in the first Ansan Secksugol Village, how the gardens were formed in the second Ansan Secksugol Village, and the relationship between the third Ansan Secksugol village and the villagers. This explores the process of perception change between individuals and individuals, individuals, neighbors, neighbors and neighbors due to the spatial transformation of the village gardens on private property. Research methods include theory and literature survey. We could understand the way of life and the local context of the past residents using the field survey and analysis and the interview survey (interview). We also typed the gardens to see the process of the village gardens. The masonry gardens garden has three space changes in total during the project period. First, the garden composition that causes the greatest change in spatial transformation is reflected in the location and area of the garden affected by the physical characteristics and environment of the village. It is necessary to dismantle some of the existing structures or give out some of the private space to the public. In other words, space changes not only from physical changes but also from private to public, giving space to all, thus creating a space for dialogue with neighbors. Second, after the creation of the garden, the residents voluntarily conduct garden activities to maintain and manage the garden, and the phenomenon of this change can be called maintenance of the garden. According to this period, the maintenance and destruction of the garden is decided, and the attachment to the garden is revealed. There are limitations in arranging garden facilities because the garden activity is mainly made by private expense, and there is not much information about gardening, and it shows inexperience. However, there are also suggestions to expand the range of gardens for each garden, to grow more plants, and to solve the problems of the villages. In other words, this garden activity can be regarded as an act expressing the desire of the individual. In addition, it is a space in which people meet with their neighbors in each garden, share their daily life and exchange information. Third, according to the maintenance of the garden, the expansion of the space where the last change of the space occurred was supported by public funds, and the space was changed. Area was added or facilities and structures were added, which were done according to the requirements of the gardener. Unsupported places changed in the people's personal budget. These changes show the persistent desire and demand of the residents for the continuing garden, which reflects the attitude and awareness of the garden. The village gardens in Ansan Secksugol Village have become a daily culture as a result of long maintenance and management. It is a poor residential environment, but it has shown sustainability in a space of one's individuality and constantly managing with affection. Unlike the community gardens that are created in the public, this is the specialty of the Ansan Secksugol Village gardens built on the private land. However, the village gardens can influence the deterioration of relations between residents by occupying and modifying space. Essentially, understanding and consideration should be premised, and the consent of residents was most important. In the village where there is no public space, there are many places where the garden is located as a base of the village. This study is a case showing the possibility of voluntary residents to provide the basis for sustainable urban revitalization policy which is operated in the village and the national level. Furthermore, the significance and value of the garden introduced as an alternative to the urban regeneration project can be grasped.๊ณผ๊ฑฐ์˜ ๋„์‹œ์žฌ์ƒ์€ ๊ณต๊ฐ„์„ ์žฌ์ƒ์‹œํ‚ค๋Š” ๊ฒƒ์ด ์ฃผ์š” ๋ชฉ์ ์ด์—ˆ๋‹ค๋ฉด ํ˜„์žฌ๋Š” ์ฃผ๋ฏผ์ด ์Šค์Šค๋กœ ๋‚˜์„œ์„œ ์ผ์ƒ์ƒํ™œํ™˜๊ฒฝ์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ  ๊ฐœ์„ ํ•ด ๋‚˜๊ฐ€๋Š” ์ฃผ๋ฏผ ์ฃผ๋„ํ˜•์˜ ๊ฐœ์„ ์‚ฌ์—…์œผ๋กœ ๋ณ€ํ•˜๊ณ  ์žˆ๋‹ค . ์ด๋Š” ์ฃผ๋ฏผ์˜ ์ธ์‹๋ณ€ํ™”์™€ ํ•ด์ฒด๋˜์—ˆ๋˜ ๊ณต๋™์ฒด ๊ด€๊ณ„ ํ˜•์„ฑ ๋ฐ ํšŒ๋ณต์„ ์ค‘์‹ฌ์œผ๋กœ ์ถ”์ง„๋˜์—ˆ๊ณ , ์žฅ๊ธฐ์ ์œผ๋กœ ์ง€์†๊ฐ€๋Šฅํ•œ ๋ฐฉ๋ฒ•์„ ๋ชจ์ƒ‰ํ•˜์˜€๋‹ค. ์ด ์ค‘ ์ •์›์€ ๋„์‹œ์žฌ์ƒ์˜ ํ•ด๊ฒฐ์ฑ…์œผ๋กœ ์ œ์‹œ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋„์‹œ๊ฐ€ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๋ฌผ๋ฆฌ์ ์ธ ํŠน์ง•์ด ์—ด์•…ํ•˜๊ณ , ๊ณต๊ณต๊ณต๊ฐ„๋ถ€์กฑ์œผ๋กœ ๊ต๋ฅ˜๊ฐ€ ์—†์—ˆ๋˜ ๊ณณ์— ๋„์‹œ์žฌ์ƒ์‚ฌ์—…์˜ ์ผํ™˜์œผ๋กœ ๋งˆ์„์ •์›์„ ๋„์ž…ํ•œ ์•ˆ์‚ฐ ์„์ˆ˜๊ณจ ๋งˆ์„์„ ์‚ฌ๋ก€ ๋Œ€์ƒ์ง€๋กœ ํ•˜์˜€๋‹ค. ์ด๊ณณ์€ ์•ˆ์‚ฐ์‹œ์˜ ์„ฑ์žฅ๊ณผ ํ•จ๊ป˜ ์กฐ์„ฑ๋œ ๋งˆ์„๋กœ ๊ณต๋‹จ ๋ฐฐํ›„๋„์‹œ์˜ ํ˜•์ƒ์„ ๊ฐ–์ถ˜ ๊ณณ์ด๋‹ค. ๊ณ ๋ฐ€๋„์˜ ๊ณ„ํš์œผ๋กœ 5์ธต ๋ฏธ๋งŒ์ธ ๋‹ค์„ธ๋Œ€ยท๋‹ค๊ฐ€๊ตฌ ๊ฑด๋ฌผ์ด ๋ฐ€์ง‘๋˜์–ด ์žˆ๊ณ , ์˜ค๋ž˜๋œ ๋ถˆ๋Ÿ‰์ฃผ๊ฑฐ์ง€๊ฐ€ ๋Œ€๋ถ€๋ถ„์ด๋ผ ์—ด์•…ํ•œ ํ™˜๊ฒฝ์„ ๊ฐ–๊ณ  ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ๋Œ€์ƒ์ง€ ๋‚ด ์ฃผ๋ฏผ์€ ์ธ๊ทผ๋„์‹œ์™€ ์ง€๋ฐฉ์—์„œ ์ด์ฃผ ํ•ด์˜จ ์‚ฌ๋žŒ๋“ค์ด ๋Œ€๋ถ€๋ถ„์ด์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ณณ์— 2007๋…„์— ๋งˆ์„์ •์› 4๊ฐœ์†Œ๊ฐ€ ์กฐ์„ฑ๋˜์—ˆ๊ณ , 10์—ฌ๋…„์ด ์ง€๋‚œ ํ˜„์žฌ 38๊ฐœ์˜ ๋งˆ์„์ •์›์ด ์กฐ์„ฑ๋˜์—ˆ๋‹ค. ๊ฐœ์ธ์‚ฌ์œ ์ง€์— ์กฐ์„ฑํ•˜๋Š” ๊ฐœ์ธ์ •์›(24๊ฐœ์†Œ), ๊ณต๊ณต์‹œ์„ค์— ์กฐ์„ฑํ•˜๋Š” ๊ณต๊ณต์ •์›(4๊ฐœ์†Œ)์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๊ณ , ์žํˆฌ๋ฆฌ๊ณต๊ฐ„์„ ํ™œ์šฉํ•œ ๋ฏธ๋‹ˆ์ •์›(10๊ฐœ์†Œ)์œผ๋กœ ๊ตฌ๋ถ„๋˜๋ฉฐ, ์ด์ค‘ 24๊ฐœ์˜ ๋งˆ์„์ •์›(๊ฐœ์ธ์ •์› 17๊ฐœ์†Œ, ๊ณต๊ณต์ •์› 4๊ฐœ์†Œ, ๋ฏธ๋‹ˆ์ •์› 3๊ฐœ์†Œ)์ด ์œ ์ง€๋˜ ์–ด์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์„ ์ค‘์‹ฌ์œผ๋กœ ์ €์ธต์ฃผ๊ฑฐ์ง€ ๋‚ด์— ๋ฐœ์ƒํ•œ ๊ณต๊ฐ„์˜ ๋ณ€์šฉ๊ณผ ์ฃผ๋ฏผ์˜์‹์— ๋Œ€ํ•œ ์˜๋ฏธ๋ฅผ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ฒซ์งธ ์„์ˆ˜๊ณจ ๋งˆ์„์— ์™œ ์ •์›์ด ํ˜•์„ฑ ๋˜์—ˆ๋Š”์ง€, ๋‘˜์งธ ์„์ˆ˜๊ณจ ๋งˆ์„์— ์–ด๋–ป๊ฒŒ ์ •์›์ด ํ˜•์„ฑ๋˜์—ˆ๋Š”์ง€, ์…‹์งธ ์„์ˆ˜๊ณจ๋งˆ์„๊ณผ ์ฃผ๋ฏผ, ๋งˆ์„์ •์›์˜ ๊ด€๊ณ„์„ฑ์„ ๊ณต๊ฐ„ ๋ณ€์šฉ์˜ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋Š” ๊ฐœ์ธ ์‚ฌ์œ ์ง€์— ์กฐ์„ฑ๋˜๋Š” ๋งˆ์„์ •์›์˜ ๊ณต๊ฐ„๋ณ€์šฉ์œผ๋กœ ์ธํ•œ ๊ฐœ์ธ๊ณผ ๊ฐœ์ธ, ๊ฐœ์ธ๊ณผ ์ด์›ƒ, ์ด์›ƒ๊ณผ ์ด์›ƒ ๊ฐ„์˜์ธ์‹๋ณ€ํ™” ๊ณผ์ •์„ ํƒ๊ตฌํ•  ์ˆ˜ ์žˆ๋‹ค. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ์ด๋ก  ๋ฐ ๋ฌธํ—Œ์กฐ์‚ฌ. ํ˜„์žฅ์กฐ์‚ฌ ๋ฐ ๋ถ„์„, ๋ฉด์ ‘์กฐ์‚ฌ(์ธํ„ฐ๋ทฐ)๋“ฑ์„ ํ™œ์šฉํ•˜์—ฌ ๋Œ€์ƒ์ง€์˜ ๊ณผ๊ฑฐ ์ฃผ๋ฏผ๋“ค์˜ ์‚ถ์˜ ๋ฐฉ์‹๊ณผ ์ง€์—ญ๋งฅ๋ฝ์„ ์ดํ•ดํ•˜์˜€๊ณ , ๋‹น์‹œ ๋งˆ์„์ •์›์— ๋Œ€ํ•œ ์ธ์‹๊ณผ ํƒœ๋„๋ฅผ ํŒŒ์•… ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ๋งˆ์„์ •์›์˜ ๊ณผ์ •์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ์ •์›์„ ์œ ํ˜•ํ™” ํ•˜์˜€๋‹ค. ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์€ ์‚ฌ์—…๊ธฐ๊ฐ„ ๋™์•ˆ ์ด 3๋ฒˆ์˜ ๊ณต๊ฐ„ ๋ณ€์šฉ์ด ๋ฐœ์ƒํ•œ๋‹ค. ์ฒซ์งธ, ๊ณต๊ฐ„ ๋ณ€์šฉ ์–‘์ƒ ์ค‘ ๊ฐ€์žฅ ํฐ ๋ณ€ํ™”๋ฅผ ๋ฐœ์ƒ์‹œํ‚ค๋Š” ์ •์›์กฐ์„ฑ์€ ๋งˆ์„์ด ๊ฐ–๊ณ  ์žˆ๋˜ ๋ฌผ๋ฆฌ์ ์ธ ํŠน์„ฑ ๋ฐ ํ™˜๊ฒฝ์— ์˜ํ–ฅ์„ ๋ฐ›์•„ ์ •์›์˜ ์œ„์น˜์„ ์ •๊ณผ ๋ฉด์ ์— ๋ฐ˜์˜๋œ๋‹ค. ์ด๋Š” ๊ธฐ์กด์˜ ๊ตฌ์กฐ๋ฌผ ์ผ๋ถ€๋ฅผ ํ—ˆ๋ฌผ๊ฑฐ๋‚˜, ๊ฐœ์ธ ๊ณต๊ฐ„์˜ ์ผ๋ถ€๋ฅผ ๊ณต๊ณต์—๊ฒŒ ๋‚ด์–ด์ฃผ์–ด์•ผ ํ•œ๋‹ค. ์ฆ‰, ๊ณต๊ฐ„์ด ๋ฌผ๋ฆฌ์ ์ธ ๋ณ€ํ™” ๋ฟ ์•„๋‹ˆ๋ผ ์‚ฌ์ ์˜์—ญ์—์„œ ๊ณต ์ ์˜์—ญ์œผ๋กœ ๋ณ€ํ™”ํ•˜๋ฉด์„œ ๋ชจ๋‘์—๊ฒŒ ๊ณต๊ฐ„์„ ๋‚ด์–ด์ฃผ๊ฒŒ ๋˜๊ณ , ์ด์— ๋”ฐ๋ผ ์ด์›ƒ๊ณผ์˜ ๋Œ€ํ™”๋ฅผ ํ•  ์ˆ˜ ์žˆ๋Š” ์žฅ์„ ๋งŒ๋“œ๋Š” ์—ญํ• ์„ ํ•œ๋‹ค. ๋‘˜์งธ, ์ •์›์ด ์กฐ์„ฑ๋˜๊ณ  ๋‚œ ํ›„ ์ฃผ๋ฏผ๋“ค์€ ์ •์›์˜ ์œ ์ง€ยท๊ด€๋ฆฌ๋ฅผ ์œ„ํ•˜์—ฌ ์ž๋ฐœ์ ์ธ ์ •์›ํ™œ๋™์„ ํ•˜๋Š”๋ฐ, ์ด ๋ณ€์šฉ์˜ ํ˜„์ƒ์„ ์ •์›์œ ์ง€๊ด€๋ฆฌ๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์‹œ๊ธฐ์— ๋”ฐ๋ผ ์ •์›์˜ ์œ ์ง€ ๋ฐ ๋ฉธ์‹ค์ด ์ •ํ•ด์ง€๋ฉฐ, ์ •์›์— ๋Œ€ํ•œ ์• ์ฐฉ์‹ฌ์ด ๋“œ๋Ÿฌ๋‚˜๊ธฐ๋„ ํ•œ๋‹ค. ์ฃผ๋กœ ๊ฐœ์ธ์‚ฌ๋น„๋กœ ์ •์›ํ™œ๋™์ด ์ด๋ฃจ์–ด์ ธ ์ •์› ์‹œ์„ค๋ฌผ์„ ๋ฐฐ์น˜ํ•˜๋Š”๋ฐ ํ•œ๊ณ„์ ์ด ์žˆ์œผ๋ฉฐ, ์ •์›๊ฐ€๊พธ๊ธฐ์— ๋Œ€ํ•œ ์ •๋ณด๊ฐ€ ๋งŽ์ง€ ์•Š์•„ ๋ฏธ์ˆ™ํ•จ์„ ๋ณด์ธ๋‹ค. ํ•˜์ง€๋งŒ ๊ฐ ์ •์›๋งˆ๋‹ค ์ •์›์˜ ๋ฒ”์œ„๋ฅผ ๋„“ํ˜€์„œ ๋” ๋งŽ์€ ์‹๋ฌผ์„ ํ‚ค์šฐ๊ณ , ์ด๋ฅผ ํ™œ์šฉํ•ด์„œ ๋งˆ์„์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋ ค๋Š” ๋ฐฉ์•ˆ๋„ ์ œ์‹œ๋˜๊ธฐ๋„ ํ•œ๋‹ค. ์ฆ‰, ์ด๋Ÿฌํ•œ ์ •์›ํ™œ๋™์€ ๊ฐœ์ธ์˜ ์š•๊ตฌ๋ฅผ ํ‘œํ˜„ํ•˜๋Š” ํ–‰์œ„๋ผ๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๊ฐ ์ •์›์—์„œ ์ด์›ƒ๊ณผ์˜ ๋งŒ๋‚จ์ด ์ด๋ฃจ์–ด์ง€๊ณ , ์„œ๋กœ์˜ ์ผ์ƒ์„ ๊ณต์œ ํ•˜๊ณ  ์ •๋ณด๋ฅผ ๊ต๋ฅ˜ํ•˜๋Š” ํ–‰์œ„๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ๊ณต๊ฐ„์ด๋ผ๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์…‹์งธ, ์ •์›์œ ์ง€๊ด€๋ฆฌ์— ๋”ฐ๋ผ์„œ ๊ณต๊ฐ„์˜ ๋งˆ์ง€๋ง‰ ๋ณ€ํ™”๊ฐ€ ๋ฐœ์ƒํ•œ ์ •์›ํ™•์žฅ์€ ๊ณต์ ์ž๊ธˆ์„ ์ง€์›๋ฐ›์•„์„œ ๊ณต๊ฐ„์˜ ๋ณ€ํ™”๊ฐ€ ๋ฐœ์ƒํ•˜์˜€๋‹ค. ๋ฉด์ ์ด ์ถ”๊ฐ€ ๋˜๊ฑฐ๋‚˜ ์‹œ์„ค๋ฌผ, ๊ตฌ์กฐ๋ฌผ์ด ์ถ”๊ฐ€ ์„ค์น˜๋˜์—ˆ๊ณ  ์ด๋Š” ์ •์› ์†Œ์œ ์ฃผ์˜ ์š”๊ตฌ์— ๋”ฐ๋ผ์„œ ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ง€์›์„ ๋ฐ›์ง€ ์•Š์€ ๊ณณ๋“ค์€ ์ฃผ๋ฏผ๋“ค์˜ ๊ฐœ์ธ ์˜ˆ์‚ฐ ์•ˆ์—์„œ ๋ณ€ํ™”๊ฐ€ ์ผ์–ด๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ์ฃผ๋ฏผ๋“ค์ด ๊ณ„์†๋˜๋Š” ์ •์›์— ๋Œ€ํ•œ ์ง€์†์ ์ธ ๊ฐˆ๋ง๊ณผ ์š”๊ตฌ๋ฅผ ๋ณด์—ฌ์ฃผ๋ฉฐ, ์ด๋Š” ์ •์›์— ๋Œ€ํ•œ ํƒœ๋„์™€ ์ธ์‹์ด ๋ฐ˜์˜๋˜์—ˆ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์„์ˆ˜๊ณจ ๋งˆ์„์—์„œ ๋งˆ์„์ •์›์€ ์˜ค๋žซ๋™์•ˆ ์œ ์ง€ํ•˜๊ณ  ๊ด€๋ฆฌํ•ด์˜จ ๊ฒฐ๊ณผ๋กœ ์ผ์ƒ์˜ ๋ฌธํ™”๊ฐ€ ๋˜์—ˆ๋‹ค. ์—ด์•…ํ•œ ์ฃผ๊ฑฐ ํ™˜๊ฒฝ์ด์ง€๋งŒ ๊ฐœ์ธ์˜ ํ•œ ๊ณต๊ฐ„์—์„œ ์• ์ฐฉ์‹ฌ์„ ๊ฐ€์ง€๊ณ  ๋Š์ž„์—†์ด ๊ด€๋ฆฌํ•˜๋Š” ์ง€์†๊ฐ€๋Šฅํ•จ์„ ๋ณด์—ฌ์คฌ๋‹ค. ์ด๋Š” ๊ณต๊ณต์— ์กฐ์„ฑ๋˜๋Š” ๊ณต๋™์ฒด์ •์›๊ณผ ๋‹ฌ๋ฆฌ ์‚ฌ์œ ์ง€์— ์กฐ์„ฑ๋œ ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์˜ ํŠน์ˆ˜์„ฑ์„ ๋“ค ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๋งˆ์„์ •์›์€ ๊ณต๊ฐ„์„ ์ ์œ ํ•˜๊ณ  ๊ฐœ์กฐํ•จ์œผ๋กœ ์ฃผ๋ฏผ๋“ค ๊ฐ„์˜ ๊ด€๊ณ„ ์•…ํ™”์— ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค. ํ•„์ˆ˜์ ์œผ๋กœ ์ดํ•ด์™€ ๋ฐฐ๋ ค๊ฐ€ ์ „์ œ๋˜์–ด์•ผ ํ•˜๊ณ , ์ฃผ๋ฏผ๋“ค์˜ ๋™์˜๊ฐ€ ๊ฐ€์žฅ ์ค‘์š”ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ณต๊ณต๊ณต๊ฐ„์ด ์—†๋˜ ๋งˆ์„์— ์ •์›์ด ๋งˆ์„์˜ ๊ฑฐ์ ์œผ๋กœ ์—ฌ๋Ÿฌ ๊ณณ์ด ์กด์žฌํ•˜๊ฒŒ ๋˜์–ด ์ฃผ๋ฏผ๋“ค์ด ์ƒํ˜ธ ๊ต๋ฅ˜ํ•˜๋Š” ์žฅ์ด ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ํ˜•์„ฑ๋˜๋Š” ๊ฒƒ๋„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํ–ฅํ›„ ๊ตญ๊ฐ€์™€ ์ง€์ž์ฒด ์ฐจ์›์—์„œ ๋งˆ์„์ •์›์ด ์šด์˜๋˜๊ณ  ์žˆ๋Š” ๋„์‹œ์žฌ์ƒ ์ •์ฑ…์„ ์ง€์†๊ฐ€๋Šฅํ•˜๊ฒŒ ์ถ”์ง„ํ•  ์ˆ˜ ์žˆ๋Š” ๊ทผ๊ฑฐ๋ฅผ ์ œ๊ณตํ•˜๋Š”๋ฐ ์ž๋ฐœ์ ์ธ ์ฃผ๋ฏผ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ฃผ๋Š” ์‚ฌ๋ก€๋ผ๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋‚˜์•„๊ฐ€ ๋„์‹œ์žฌ์ƒ์‚ฌ์—…์˜ ๋Œ€์•ˆ์œผ๋กœ์„œ ๋„์ž…๋œ ์ •์›์˜ ์˜์˜์™€ ๊ฐ€์น˜๋ฅผ ํŒŒ์•… ํ•  ์ˆ˜ ์žˆ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1์ ˆ. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  01 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 03 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  03 2์ ˆ. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 04 1. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 04 2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 05 3์ ˆ. ์—ฐ๊ตฌ ํ๋ฆ„๋„ 09 ์ œ 2 ์žฅ ์ด๋ก  ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ 1์ ˆ. ๋งˆ์„์ •์›์— ๋Œ€ํ•œ ์ดํ•ด 10 1. ๋งˆ์„์ •์›์˜ ๊ฐœ๋… 10 2. ๊ณต๊ฐ„์˜ ์˜์—ญ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๋งˆ์„์ •์›์˜ ์†์„ฑ 11 2์ ˆ. ์ €์ธต์ฃผ๊ฑฐ์ง€์™€ ๋งˆ์„์ •์›์˜ ๊ด€๊ณ„ 13 1. ์ €์ธต์ฃผ๊ฑฐ์ง€์˜ ์™ธ๋ถ€๊ณต๊ฐ„์˜ ์†์„ฑ 13 2. ์ €์ธต์ฃผ๊ฑฐ์ง€์—์„œ ๋งˆ์„์ •์›์˜ ๊ณต๊ฐ„์  ์˜๋ฏธ 14 3์ ˆ. ์„ ํ–‰์—ฐ๊ตฌ์˜ ๊ณ ์ฐฐ 16 1. ์•ˆ์‚ฐ ์„์ˆ˜๊ณจ ๋งˆ์„(์„ ๋ถ€2๋™)์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 16 2. ์ฃผ๊ฑฐ๊ณต๊ฐ„๊ณผ ์ •์›์— ๋Œ€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ 17 4์ ˆ. ๋ถ„์„์˜ ํ‹€ 19 ์ œ 3 ์žฅ. ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์˜ ์ดํ•ด 1์ ˆ. ์„์ˆ˜๊ณจ ๋งˆ์„์˜ ํ˜•์„ฑ ๋ฐ ํ˜„ํ™ฉ 20 1. ๋Œ€์ƒ์ง€์˜ ํ˜•์„ฑ 20 2. ๋Œ€์ƒ์ง€์˜ ๊ฐœ์š” ๋ฐ ์ผ๋ฐ˜ํ˜„ํ™ฉ 22 2์ ˆ. ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์˜ ๋ฐฐ๊ฒฝ ๋ฐ ์กฐ์„ฑ๊ณผ์ • 30 1. ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์˜ ํ˜•์„ฑ๋ฐฐ๊ฒฝ 30 2. ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์˜ ์กฐ์„ฑ๊ณผ์ • 32 3. ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์˜ ์‹œ๊ธฐ๋ณ„ ์ฃผ์š”ํ™œ๋™ 36 3์ ˆ. ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์˜ ํ˜„ํ™ฉ 40 1. ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์˜ ๋ถ„ํฌ 40 2. ์„์ˆ˜๊ณจ ๋งˆ์„์ •์›์— ๋”ฐ๋ฅธ ํ˜‘๋ ฅ๊ด€๊ณ„ 47 ์ œ 4 ์žฅ. ๋งˆ์„์ •์›์˜ ๋„์ž… ์ดํ›„์˜ ๋ณ€ํ™” 1์ ˆ. ๋งˆ์„์ •์› ๊ณต๊ฐ„๋ณ€์šฉ์˜ ๋ฐœ์ƒ ๋ฐฐ๊ฒฝ 53 1. ๋Œ€์ƒ์ง€๊ฐ€ ๊ฐ–๊ณ ์žˆ๋Š” ํ™˜๊ฒฝ 54 2. ๋งˆ์„๋ฌธ์ œ๋กœ ์กฐ์งํ™œ๋™ ๊ฒฝํ—˜์ด ์žˆ๋Š” ์ฃผ๋ฏผ๋“ค 59 2์ ˆ. ๋งˆ์„์ •์› ๊ณต๊ฐ„๋ณ€์šฉ์–‘์ƒ 60 1. ๊ณต๊ฐ„์˜ ๋ณ€์šฉ : ์ •์›์กฐ์„ฑ 60 2. ๊ณต๊ฐ„์˜ ๋ณ€์šฉ : ์ •์›์œ ์ง€๊ด€๋ฆฌ 74 3. ๊ณต๊ฐ„์˜ ๋ณ€์šฉ : ์ •์›ํ™•์žฅ 80 3์ ˆ. ๋งˆ์„์ •์› ๋„์ž… ๋ฐ ๋ณ€์šฉ๊ณผ์ • ํ•ด์„ 88 1. ๋งˆ์„์ •์›์„ ๊ฐ€๊พธ๋Š” ์„์ˆ˜๊ณจ ์ฃผ๋ฏผ๋“ค 88 2. ํ•ต์‹ฌ์ธ๋ฌผ์˜ ์—ญํ• ๊ณผ ์˜ํ–ฅ๋ ฅ 92 3. ์ €์ธต์ฃผ๊ฑฐ์ง€์—์„œ์˜ ๋งˆ์„์ •์›์˜ ์—ญํ•  95 ์ œ 5 ์žฅ. ๊ฒฐ๋ก  ๋ฐ ์‹œ์‚ฌ์  100 [์ฐธ๊ณ ๋ฌธํ—Œ] [๋ถ€๋ก]Maste

    ํ•œ๊ตญ์–ด ํ…์ŠคํŠธ ๋…ผ์ฆ ๊ตฌ์กฐ์˜ ์ž๋™ ๋ถ„์„ ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์–ธ์–ดํ•™๊ณผ ์–ธ์–ดํ•™์ „๊ณต, 2016. 2. ์‹ ํšจํ•„.์ตœ๊ทผ ์˜จ๋ผ์ธ ํ…์ŠคํŠธ ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋Œ€์ค‘์˜ ์˜๊ฒฌ์„ ๋ถ„์„ํ•˜๋Š” ์ž‘์—…์ด ํ™œ๋ฐœํžˆ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ž‘์—…์—๋Š” ์ฃผ๊ด€์  ๋ฐฉํ–ฅ์„ฑ์„ ๊ฐ–๋Š” ํ…์ŠคํŠธ์˜ ๋…ผ์ฆ ๊ตฌ์กฐ์™€ ์ค‘์š” ๋‚ด์šฉ์„ ํŒŒ์•…ํ•˜๋Š” ๊ณผ์ •์ด ํ•„์š”ํ•˜๋ฉฐ, ์ž๋ฃŒ์˜ ์–‘๊ณผ ๋‹ค์–‘์„ฑ์ด ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•˜๋ฉด์„œ ๊ทธ ๊ณผ์ •์˜ ์ž๋™ํ™”๊ฐ€ ๋ถˆ๊ฐ€ํ”ผํ•ด์ง€๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ •์ฑ…์— ๋Œ€ํ•œ ์ฐฌ๋ฐ˜ ์˜๊ฒฌ์œผ๋กœ ๊ตฌ์„ฑ๋œ ํ•œ๊ตญ์–ด ํ…์ŠคํŠธ ์ž๋ฃŒ๋ฅผ ์ง์ ‘ ๊ตฌ์ถ•ํ•˜๊ณ , ๊ธ€์„ ๊ตฌ์„ฑํ•˜๋Š” ๊ธฐ๋ณธ ๋‹จ์œ„๋“ค ์‚ฌ์ด์˜ ๋‹ดํ™” ๊ด€๊ณ„์˜ ์œ ํ˜•์„ ์ •์˜ํ•˜์˜€๋‹ค. ํ•˜๋‚˜์˜ ๋งฅ๋ฝ ์•ˆ์—์„œ ๋‘ ๊ฐœ์˜ ๋ฌธ์žฅ ํ˜น์€ ์ ˆ์ด ์„œ๋กœ ๊ด€๊ณ„๋ฅผ ๊ฐ–๋Š”์ง€, ๊ด€๊ณ„๋ฅผ ๊ฐ–๋Š”๋‹ค๋ฉด ์„œ๋กœ ๋™๋“ฑํ•œ ๊ด€๊ณ„์ธ์ง€, ๊ทธ๋ ‡์ง€ ์•Š์€ ๊ฒฝ์šฐ ์–ด๋Š ๋ฌธ์žฅ(์ ˆ)์ด ๋” ์ค‘์š”ํ•œ ๋ถ€๋ถ„์œผ๋กœ์„œ ๋‹ค๋ฅธ ํ•˜๋‚˜์˜ ์ง€์ง€๋ฅผ ๋ฐ›๋Š”์ง€์˜ ๊ธฐ์ค€์— ๋”ฐ๋ผ ๋‹ดํ™” ๊ด€๊ณ„๋ฅผ ๋‘ ๊ฐœ์˜ ์ธต์œ„๋กœ ๋‚˜๋ˆ„์–ด ์ด์šฉํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ๋ณธ ๋‹จ์œ„๋“ค ์‚ฌ์ด์˜ ๊ด€๊ณ„๋Š” ๊ธฐ๊ณ„ ํ•™์Šต๊ณผ ๊ทœ์น™ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์„ ์ด์šฉํ•˜์—ฌ ์˜ˆ์ธก๋œ๋‹ค. ์ด ๋•Œ ๊ฐ ๊ธ€์˜ ์ €์ž๊ฐ€ ํ‘œํ˜„ํ•˜๊ณ ์ž ํ•˜๋Š” ์˜๋„, ์ž์‹ ์˜ ์ฃผ์žฅ์„ ๋’ท๋ฐ›์นจํ•˜๊ธฐ ์œ„ํ•ด ์ œ์‹œํ•˜๋Š” ๊ทผ๊ฑฐ์˜ ์ข…๋ฅ˜, ๊ทธ๋ฆฌ๊ณ  ๊ทธ ๊ทผ๊ฑฐ๋ฅผ ์ด๋ฃจ๋Š” ๋…ผ์ฆ ์ „๋žต ๋“ฑ์ด ํ…์ŠคํŠธ์˜ ์–ธ์–ด์  ํŠน์ง•๊ณผ ํ•จ๊ป˜ ์ค‘์š”ํ•œ ์ž์งˆ๋กœ ์ž‘์šฉ๋œ๋‹ค. ๋…ผ์ฆ์˜ ์ „๋žต์œผ๋กœ๋Š” ์˜ˆ์‹œ, ์ธ๊ณผ, ์„ธ๋ถ€ ์‚ฌํ•ญ์— ๋Œ€ํ•œ ์„ค๋ช…, ๋ฐ˜๋ณต ์„œ์ˆ , ์ •์ •, ๋ฐฐ๊ฒฝ ์ง€์‹ ์ œ๊ณต ๋“ฑ์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์ด๋“ค ์„ธ๋ถ€ ๋ถ„๋ฅ˜๋Š” ๋‹ดํ™” ๊ด€๊ณ„์˜ ๋Œ€๋ถ„๋ฅ˜๋ฅผ ๊ตฌ์„ฑํ•˜๊ณ , ๊ทธ ๋‹ดํ™” ๊ด€๊ณ„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ฐ ์“ฐ์ด๋Š” ์ž์งˆ์˜ ๊ธฐ๋ฐ˜์ด ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์ผ๋ถ€ ์–ธ์–ด์  ์ž์งˆ๋“ค์€ ๊ธฐ์กด ์—ฐ๊ตฌ๋ฅผ ์ฐธ๊ณ ํ•˜์—ฌ ํ•œ๊ตญ์–ด ์ž๋ฃŒ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํ˜•ํƒœ๋กœ ์žฌ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ํ•œ๊ตญ์–ด ์ฝ”ํผ์Šค๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ  ํ•œ๊ตญ์–ด ์—ฐ๊ตฌ์— ํŠนํ™”๋œ ์ ‘์†์‚ฌ ๋ฐ ์—ฐ๊ฒฐ์–ด์˜ ๋ชฉ๋ก์„ ๊ตฌ์„ฑํ•˜์—ฌ ์ž์งˆ ๋ชฉ๋ก์— ํฌํ•จ์‹œ์ผฐ๋‹ค. ์ด๋Ÿฌํ•œ ์ž์งˆ๋“ค์— ๊ธฐ๋ฐ˜ํ•ด์„œ ๋‹ดํ™” ๊ด€๊ณ„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๊ณผ์ •์„ ์ด ์—ฐ๊ตฌ์—์„œ ๋…์ž์ ์ธ ๋ชจ๋ธ๋กœ์„œ ์ž๋™ํ™”ํ•˜์—ฌ ์ œ์•ˆํ•˜์˜€๋‹ค. ์˜ˆ์ธก ์‹คํ—˜์˜ ๊ฒฐ๊ณผ๋ฅผ ๋ณด๋ฉด ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ •์˜ํ•˜์—ฌ ์ด์šฉํ•œ ์ž์งˆ๋“ค์€ ๊ธ์ •์ ์ธ ์ƒํ˜ธ ์ž‘์šฉ์„ ํ†ตํ•ด ๋‹ดํ™” ๊ด€๊ณ„ ์˜ˆ์ธก์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ ์ค‘์—์„œ๋„ ์ผ๋ถ€ ์ ‘์†์‚ฌ ๋ฐ ์—ฐ๊ฒฐ์–ด, ๋ฌธ์žฅ ์„ฑ๋ถ„์˜ ์œ ๋ฌด์— ๋”ฐ๋ฅธ ์˜์กด์ ์ธ ๋ฌธ์žฅ ๊ตฌ์กฐ, ๊ทธ๋ฆฌ๊ณ  ๊ฐ™์€ ๋‚ด์šฉ์„ ๋ฐ˜๋ณต ์„œ์ˆ ํ•˜๋Š”์ง€์˜ ์—ฌ๋ถ€ ๋“ฑ์ด ํŠนํžˆ ์˜ˆ์ธก์— ๊ธฐ์—ฌํ•˜์˜€๋‹ค. ํ…์ŠคํŠธ๋ฅผ ์ด๋ฃจ๋Š” ๊ธฐ๋ณธ ๋‹จ์œ„๋“ค ์‚ฌ์ด์— ์กด์žฌํ•˜๋Š” ๋‹ดํ™” ๊ด€๊ณ„๋“ค์€ ์„œ๋กœ ์—ฐ๊ฒฐ, ํ•ฉ์„ฑ๋˜์–ด ํ…์ŠคํŠธ ์ „์ฒด์— ๋Œ€์‘๋˜๋Š” ํŠธ๋ฆฌ ํ˜•ํƒœ์˜ ๋…ผ์ฆ ๊ตฌ์กฐ๋ฅผ ์ด๋ฃฌ๋‹ค. ์ด๋ ‡๊ฒŒ ์–ป์€ ๋…ผ์ฆ ๊ตฌ์กฐ์— ๋Œ€ํ•ด์„œ๋Š”, ํŠธ๋ฆฌ์˜ ๊ฐ€์žฅ ์œ„์ชฝ์ธ ๋ฃจํŠธ ๋…ธ๋“œ์— ๊ธ€์˜ ์ฃผ์ œ๋ฌธ์ด ์œ„์น˜ํ•˜๊ณ , ๊ทธ ๋ฐ”๋กœ ์•„๋ž˜ ์ธต์œ„์— ํ•ด๋‹นํ•˜๋Š” ๋ฌธ์žฅ(์ ˆ)๋“ค์ด ๊ทผ๊ฑฐ๋กœ์„œ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ๋‚ด์šฉ์„ ๋‹ด๊ณ  ์žˆ๋‹ค๊ณ  ๊ฐ€์ •ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ฃผ์ œ๋ฌธ์„ ์ง์ ‘์ ์œผ๋กœ ๋’ท๋ฐ›์นจํ•˜๋Š” ๋ฌธ์žฅ(์ ˆ)์„ ์ถ”์ถœํ•˜๋ฉด ๊ธ€์˜ ์ค‘์š” ๋‚ด์šฉ์„ ์–ป๊ฒŒ ๋œ๋‹ค. ์ด๋Š” ๊ณง ํ…์ŠคํŠธ ์š”์•ฝ ์ž‘์—…์—์„œ ์œ ์šฉํ•˜๊ฒŒ ์“ฐ์ด๋Š” ๋ฐฉ์‹์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ฃผ์ œ์— ๋”ฐ๋ฅธ ์ž…์žฅ ๋ถ„๋ฅ˜๋‚˜ ๊ทผ๊ฑฐ ์ˆ˜์ง‘ ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ๋„ ์‘์šฉ์ด ๊ฐ€๋Šฅํ•  ๊ฒƒ์ด๋‹ค.These days, there is an increased need to analyze mass opinions using on-line text data. These tasks need to recognize the argumentation schemes and main contents of subjective, argumentative writing, and the automatization of the required procedures is becoming indispensable. This thesis constructed the text data using Korean debates on certain political issues, and defined the types of discourse relations between basic units of text segments. The discourse relations are classified into two levels and four subclasses, according to the standards which determine whether the two segments are related to each other in a context, whether the relation is coordinating or subordinating, and which of the two units in a pair is supported by the other as a more important part. The relations between basic text units are predicted based on machine learning and rule-based methods. The features for the prediction of discourse relations include what the author of a text wants to claim and argumentative strategies comprising grounds for the author's claim, using linguistic properties shown in texts. The strategies for argument are observed and subcategorized into Providing Examples, Cause-and-Effects, Explanations in Detail, Restatements, Contrasts, Background Knowledge, and more. These subclasses compose a broader class of discourse relations and became the basis for features used during the classification of the relations. Some linguistic features refer to those of previous studies, they are reconstituted in a revised form which is more appropriate for Korean data. Thus, this study constructed a Korean debate corpus and a list of connectives specialized to deal with Korean texts to include in the experiment features. The automated prediction of discourse relations based on those features is suggested in this study as a unique model of argument mining. According to the results of experiments predicting discourse relations, the features defined and used in this study are observed to improve the performance of prediction tasks through positive interactions with each other. In particular, some explicit connectives, dependent sentence structures based on lack of certain components, and whether the same meanings are restated clearly contributed to the classification tasks. The discourse relations between basic text units are related and combined with each other to comprise a tree-form argumentation structure for the overall document. Regarding the argumentation structure, the topic sentence of the document is located at the root node in the tree, and it is assumed that the nodes of sentences or clauses right below the root node contain the most important contents as grounds for the topic unit. Therefore, extraction of the text segments directly supporting the topic sentence may help in obtaining the important contents in each document. This can be one of the useful methods in text summarization. Additionally, applications to various fields may also be possible, including stance classification of debate texts, extraction of grounds for certain topics, and so on.1 Introduction 1 1.1 Purposes 1 1.1.1 A Study of Korean Texts with Linguistic Cues 1 1.1.2 Detection of Argumentation Schemes in Debate Texts 2 1.1.3 Extraction of Important Content in Argumentation Schemes of Texts 2 1.2 Structure 3 2 Previous Work 5 2.1 Argumentation Mining Tasks 7 2.1.1 Argument Elements 7 2.1.2 Argumentation Schemes 9 2.2 Argumentation Schemes in Various Texts 14 2.2.1 Dialogic vs. Monologic Texts 14 2.2.2 Debate Texts vs. Other Texts 15 2.2.3 Studies in Other Languages 17 2.3 Theoretical Basis 18 2.3.1 Argumentation Theory 18 2.3.2 Discourse Theory 21 3 Identifying Argumentation Schemes in Debate Texts 25 3.1 Data Description 25 3.2 Basic Units 27 3.3 Discourse Relations 29 3.3.1 Strategies for Proving a Claim 29 3.3.2 Definition 35 4 Automatic Identification of Argumentation Schemes 41 4.1 Annotation 41 4.2 Baseline 46 4.3 Proposed Model 50 4.3.1 O vs. X Classification 51 4.3.2 Convergent Relation Rule 61 4.3.3 NN vs. NS vs. SN Classification 65 4.4 Evaluation 67 4.4.1 Measures 67 4.4.2 Results 68 4.5 Discussion 74 4.6 A Pilot Study on English Texts 81 5 Detecting Important Units 87 6 Conclusion 99 Bibliography 103 ์ดˆ๋ก 117Maste

    ์„ฑ์ธ ๊ฒฐํ•ตํ™˜์ž์˜ ๋‚™์ธ, ์‚ฌํšŒ์  ์ง€์ง€, ํšŒ๋ณต๋ ฅ์ด ์น˜๋ฃŒ์ดํ–‰์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

    Get PDF
    ๊ฐ„ํ˜ธ๋Œ€ํ•™/์„์‚ฌ๋ณธ ์—ฐ๊ตฌ๋Š” ์„ฑ์ธ ๊ฒฐํ•ตํ™˜์ž์˜ ๋‚™์ธ, ์‚ฌํšŒ์  ์ง€์ง€, ํšŒ๋ณต๋ ฅ๊ณผ ์น˜๋ฃŒ์ดํ–‰๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ทœ๋ช…ํ•˜๊ณ  ์น˜๋ฃŒ์ดํ–‰์— ๋ฏธ์น˜๋Š” ์ œ ์š”์ธ์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•œ ์„œ์ˆ ์  ์กฐ์‚ฌ ์—ฐ๊ตฌ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ์ž๋Š” ์„œ์šธ ์†Œ์žฌ ์ผ ์ข…ํ•ฉ๋ณ‘์› ํ˜ธํก๊ธฐ๋‚ด๊ณผ ์™ธ๋ž˜์— ๋‚ด์› ์ค‘์ธ ํ™˜์ž ์ค‘ ํ™œ๋™์„ฑ ํ๊ฒฐํ•ต์œผ๋กœ ์ง„๋‹จ๋ฐ›์€ ํ›„ ์•ฝ๋ฌผ์น˜๋ฃŒ๋ฅผ ์‹œ์ž‘ํ•œ์ง€ 6๊ฐœ์›” ์ด๋‚ด์˜ 20์„ธ ์ด์ƒ ์„ฑ์ธ ๋‚จ๋…€ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€์œผ๋ฉฐ, ์ž๊ฐ€๋ณด๊ณ ์‹ ๊ตฌ์กฐํ™”๋œ ์„ค๋ฌธ์ง€๋ฅผ ์ด์šฉํ•˜์—ฌ 2015๋…„ 4์›” 8์ผ๋ถ€ํ„ฐ 6์›” 4์ผ๊นŒ์ง€ ์ž๋ฃŒ์ˆ˜์ง‘์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์ด 116๋ถ€์˜ ์„ค๋ฌธ์ง€ ์ค‘ ์ฃผ์š” ๋ณ€์ˆ˜์— ๋Œ€ํ•œ ๋ฌด์‘๋‹ต์ด ํฌํ•จ๋œ 3๋ถ€์˜ ์„ค๋ฌธ์ง€๋ฅผ ์ œ์™ธํ•œ 113๋ถ€๊ฐ€ ํ†ต๊ณ„๋ถ„์„์— ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. 1. ๋Œ€์ƒ์ž์˜ ํ‰๊ท ์—ฐ๋ น์€ 49.94์„ธ๋กœ ๋‚จ์„ฑ์ด 54.9%์˜€์œผ๋ฉฐ, ๊ฒฐํ˜ผ์ƒํƒœ๋Š” ๊ธฐํ˜ผ์ด 61.9%๋ฅผ ์ฐจ์ง€ํ•˜์˜€๋‹ค. ๊ต์œก์ˆ˜์ค€์€ ๋Œ€ํ•™๊ต ์ด์ƒ์ด 77.0%, ์ง์—…์€ ๋ฌด์ง/์ฃผ๋ถ€๊ฐ€ 40.7%๋กœ ๊ฐ€์žฅ ๋งŽ์•˜๋‹ค. ๊ฒฐํ•ต ๊ณผ๊ฑฐ๋ ฅ์ด ์—†๋Š” ๋Œ€์ƒ์ž๊ฐ€ 78.8%, ๊ฒฐํ•ต ๊ฐ€์กฑ๋ ฅ์ด ์—†๋Š” ๋Œ€์ƒ์ž๊ฐ€ 76.1%๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฒฐํ•ต์•ฝ ๋ณต์šฉ๊ธฐ๊ฐ„์€ 2๊ฐœ์›” ์ดํ•˜๊ฐ€ 54.9%๋ฅผ ์ฐจ์ง€ํ•˜์˜€๊ณ , ๋™๋ฐ˜์งˆํ™˜์„ ์•“๊ณ  ์žˆ๋Š” ๋Œ€์ƒ์ž๊ฐ€ 60.2%์˜€์œผ๋ฉฐ, ๋™๋ฐ˜์งˆํ™˜ ์ค‘ ๊ณ ํ˜ˆ์••์ด 25.0%๋กœ ๋งŽ์•˜๋‹ค. ๊ฒฐํ•ต ๊ด€๋ จ ๊ต์œก์ด๋‚˜ ์ƒ๋‹ด์„ ๋ฐ›์•˜๋˜ ๊ฒฝํ—˜์ด ์—†๋Š” ๋Œ€์ƒ์ž๋Š” 92.9%์˜€๋‹ค. 2. ๋Œ€์ƒ์ž๊ฐ€ ์ง€๊ฐํ•˜๋Š” ๋‚™์ธ์˜ ํ•˜์œ„์˜์—ญ ์ค‘ ํƒ€์ธ์˜ ๊ด€์ ์— ๋Œ€ํ•œ ๋ณธ์ธ์˜ ์ง€๊ฐ์ด 37.03ยฑ9.58์ , ๊ฒฐํ•ต์— ๋Œ€ํ•œ ํ™˜์ž์˜ ๊ด€์ ์ด 30.35ยฑ9.58์ ์ด์—ˆ๋‹ค. ์‚ฌํšŒ์  ์ง€์ง€ ํ•˜์œ„์˜์—ญ ๋ณ„๋กœ๋Š” ๊ฐ€์กฑ ์ง€์ง€ 53.19ยฑ7.49์ , ์˜๋ฃŒ์ธ ์ง€์ง€ 48.99ยฑ9.76์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ํšŒ๋ณต๋ ฅ์€ 77.09ยฑ13.37์ ์ด์—ˆ๋‹ค. ๋Œ€์ƒ์ž์˜ ์น˜๋ฃŒ์ดํ–‰์€ 4.04ยฑ0.62์ ์ด์—ˆ๋‹ค. 3. ๋Œ€์ƒ์ž์˜ ๋‚™์ธ ํ•˜์œ„์˜์—ญ, ์‚ฌํšŒ์  ์ง€์ง€ ํ•˜์œ„์˜์—ญ, ํšŒ๋ณต๋ ฅ, ์น˜๋ฃŒ์ดํ–‰์˜ ์ƒ๊ด€๊ด€๊ณ„์—์„œ ๊ฒฐํ•ต์— ๋Œ€ํ•œ ํƒ€์ธ์˜ ๊ด€์ ์€ ์˜๋ฃŒ์ธ ์ง€์ง€(r=.200, p=.033)์™€ ์œ ์˜ํ•œ ์–‘์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ๊ฐ€์กฑ ์ง€์ง€๋Š” ํšŒ๋ณต๋ ฅ(r=.933, p=.000)๊ณผ ์น˜๋ฃŒ์ดํ–‰(r=.491, p=.000)๊ณผ ์œ ์˜ํ•œ ์–‘์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€๊ณ , ์˜๋ฃŒ์ธ ์ง€์ง€๋Š” ํšŒ๋ณต๋ ฅ(r=.444, p=000), ์น˜๋ฃŒ์ดํ–‰(r=.496, p=.000)๊ณผ ์œ ์˜ํ•œ ์–‘์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. 4. ๋Œ€์ƒ์ž์˜ ์น˜๋ฃŒ์ดํ–‰์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์—ฐ๋ น(ฮฒ=.455, p=.000), ๋‚™์ธ์˜ ํ•˜์œ„ ์˜์—ญ์ธ ๊ฒฐํ•ต์— ๋Œ€ํ•œ ํƒ€์ธ์˜ ๊ด€์ (ฮฒ=-.186, p=.015), ๊ฐ€์กฑ ์ง€์ง€(ฮฒ.281, p=.002), ์˜๋ฃŒ์ธ์ง€์ง€(ฮฒ=.226, p=.015)๊ฐ€ ์น˜๋ฃŒ์ดํ–‰์— ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด์ƒ์˜ ๊ฒฐ๊ณผ์—์„œ ์„ฑ์ธ ๊ฒฐํ•ตํ™˜์ž์˜ ์น˜๋ฃŒ์ดํ–‰์€ ์—ฐ๋ น, ๊ฒฐํ•ต์— ๋Œ€ํ•œ ํƒ€์ธ์˜ ๊ด€์ , ๊ฐ€์กฑ์ง€์ง€, ์˜๋ฃŒ์ธ ์ง€์ง€์— ์˜ํ–ฅ์„ ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฒฐํ•ตํ™˜์ž์˜ ์น˜๋ฃŒ์ดํ–‰ ์ด‰์ง„์„ ์œ„ํ•ด์„œ ์—ฐ๋ น์„ ๊ณ ๋ คํ•œ ์ ‘๊ทผ๊ณผ ๋‚™์ธ ๊ฐ์†Œ, ์‚ฌํšŒ์  ์ง€์ง€ ์ฆ๊ฐ€๋ฅผ ์œ„ํ•œ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•œ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค. ๋‚™์ธ์˜ ํ•˜์œ„์˜์—ญ ์ค‘ ๊ฒฐํ•ต์— ๋Œ€ํ•œ ํƒ€์ธ์˜ ๊ด€์ ์ด ์œ ์˜ํ•œ ์˜ํ–ฅ์š”์ธ์œผ๋กœ ๋‚˜ํƒ€๋‚˜, ๊ฒฐํ•ตํ™˜์ž๊ฐ€ ํƒ€์ธ์˜ ์‹œ์„ ์— ๋Œ€ํ•ด ๋ถ€์ •์ ์œผ๋กœ ๋Š๋ผ์ง€ ์•Š๊ณ  ๊ฐ๊ด€์ ์œผ๋กœ ๋ฐ”๋ผ๋ณผ ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๋Œ€์ฒ˜๋ฐฉ๋ฒ•์ด ํ™˜์ž ๊ต์œก ๋ฐ ์ƒ๋‹ด์— ๋ฐ˜์˜๋˜์–ด์•ผ ํ•˜๋ฉฐ, ํ™˜์ž๊ฐ€ ์ง€๊ฐํ•˜๋Š” ์˜๋ฃŒ์ธ์˜ ์ง€์ง€๋ฅผ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์„ ๋ชจ์ƒ‰ํ•ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค.ope

    The Abolition of Veterans' Extra Points System and Its Impact on Gender Differences in Public Sector Employment

    No full text
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์ œํ•™๋ถ€, 2017. 2. ๋ฅ˜๊ทผ๊ด€.๊ตฐ๊ฐ€์‚ฐ์ ์ œ๋Š” ์ œ๋Œ€ ๊ตฐ์ธ์ด ๊ณต๋ฌด์› ์ž„์šฉ ์‹œํ—˜์— ์‘์‹œํ•  ๊ฒฝ์šฐ 3~5%์˜ ๊ฐ€์‚ฐ์ ์„ ๋ถ€์—ฌํ•˜๋˜ ์ œ๋„์ด๋‹ค. ๊ตฐ๊ฐ€์‚ฐ์ ์ œ๋ฅผ ๊ทœ์ •ํ•˜๋Š” ๋ฒ•๋ฅ ์€ ํ‰๋“ฑ๊ถŒ, ๊ณต๋ฌด๋‹ด์ž„๊ถŒ ๋“ฑ์„ ์นจํ•ดํ•œ๋‹ค ํ•˜์—ฌ ํ—Œ๋ฒ•์žฌํŒ์†Œ์—์„œ ์œ„ํ—Œ ํŒ๊ฒฐ์„ ๋ฐ›๊ณ  1999๋…„ 12์›” ํ์ง€๋˜์—ˆ๋‹ค. ์ด์žฌํ˜ธ(2014)๋Š” ๊ทธ์˜ ๋…ผ๋ฌธ์—์„œ ๊ตฐ๊ฐ€์‚ฐ์ ์ œ์˜ ํ์ง€๊ฐ€ ์—ฌ์„ฑ์— ๋น„ํ•ด ๋‚จ์„ฑ์˜ ๊ณต๊ณต๋ถ€๋ฌธ ์ทจ์—… ํ™•๋ฅ ์„ ์ถ”๊ฐ€์ ์œผ๋กœ ๊ฐ์†Œ์‹œ์ผฐ๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ•ด๋‹น ๋…ผ๋ฌธ์ด ์‚ฌ์šฉํ•œ ํ‘œ๋ณธ ๋ฐ ๋ณ€์ˆ˜์— ๋ถ€์ ์ ˆํ•œ ๋ถ€๋ถ„์ด ๋งŽ์•„ ์ด๋ฅผ ๊ทธ๋Œ€๋กœ ๋ฐ›์•„๋“ค์ด๊ธฐ๋Š” ์–ด๋ ต๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•ด๋‹น ๋…ผ๋ฌธ์˜ ๋ฌธ์ œ์ ์„ ํ•ด๊ฒฐํ•จ์œผ๋กœ์จ ๊ตฐ๊ฐ€์‚ฐ์ ์ œ์˜ ํ์ง€๊ฐ€ ๋‚จ๋…€ ์‹ ๊ทœ ์ทจ์—…์ž์˜ ๊ณต๊ณต๋ถ€๋ฌธ ์ทจ์—… ํ™•๋ฅ ์— ๋ฏธ์นœ ์˜ํ–ฅ์„ ๋ณด๋‹ค ์ •ํ™•ํžˆ ํ™•์ธํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด์ค‘์ฐจ๋ถ„๋ฒ•(Difference-in-Differences)์„ ์‚ฌ์šฉํ•œ ๋ณธ ์—ฐ๊ตฌ์˜ ๋ถ„์„ ๊ฒฐ๊ณผ, ๊ตฐ๊ฐ€์‚ฐ์ ์ œ์˜ ํ์ง€๊ฐ€ ์—ฌ์„ฑ์— ๋น„ํ•ด ๊ตฐ๋ณต๋ฌด์ž์˜ ๊ณต๊ณต๋ถ€๋ฌธ ์ „๋ฐ˜์— ๋Œ€ํ•œ ์ทจ์—… ํ™•๋ฅ ์„ ์ถ”๊ฐ€์ ์œผ๋กœ ๊ฐ์†Œ์‹œํ‚ค์ง€๋Š” ์•Š์•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ณต๊ณต๋ถ€๋ฌธ์˜ ์ผ๋ถ€๋ถ„์ธ ๊ณต๊ณตํ–‰์ •๋ถ€๋ฌธ์—์„œ๋Š” ์ „๋ฌธ๋Œ€ ๋ฐ ๋Œ€ํ•™๊ต ์กธ์—…์ž์— ํ•œํ•˜์—ฌ ๊ตฐ๊ฐ€์‚ฐ์ ์ œ์˜ ํ์ง€๊ฐ€ ์—ฌ์„ฑ์— ๋น„ํ•ด ๊ตฐ๋ณต๋ฌด์ž์˜ ์ทจ์—… ํ™•๋ฅ ์„ ์ถ”๊ฐ€์ ์œผ๋กœ ๊ฐ์†Œ์‹œํ‚ค๋Š” ์š”์ธ์œผ๋กœ ์ž‘์šฉํ•˜์˜€๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.2 ์„ ํ–‰ ์—ฐ๊ตฌ ์†Œ๊ฐœ 2 1.3 ์„ ํ–‰ ์—ฐ๊ตฌ์˜ ๋ฌธ์ œ์  5 ์ œ 2 ์žฅ ์‹ค ์ฆ ๋ถ„ ์„ 8 2.1 ์„ ํ–‰ ์—ฐ๊ตฌ์˜ ๋ฌธ์ œ์  ํ™•์ธ 8 2.2 ์—ฐ๊ตฌ ๋ชจํ˜• ์‹ค์ฆ ๋ถ„์„ 16 2.2.1 ์—ฐ๊ตฌ ๊ฐ€์„ค 16 2.2.2 ๋ชจํ˜• ๋ฐ ๋ณ€์ˆ˜ ์„ค๋ช… 16 2.2.3 ์ž๋ฃŒ ๋ฐ ์ถ”์ • ๊ฒฐ๊ณผ 19 2.2.4 ๊ฐ•๊ฑด์„ฑ ํ™•์ธ(Robustness Check) 22 ์ œ 3 ์žฅ ๊ฒฐ ๋ก  30 ์ฐธ๊ณ ๋ฌธํ—Œ 32 Abstract 33Maste

    Study on the context-based reading education of modernist poetry

    No full text
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ตญ์–ด๊ต์œก๊ณผ(๊ตญ์–ด๊ต์œก์ „๊ณต), 2011.2. ์œค์—ฌํƒ.Maste

    Evaluation of dietary habit and hilcobacter pylori infection in control and early gastric cancer patient

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์‹ํ’ˆ์˜์–‘ํ•™๊ณผ,2001.Docto

    ์นจ์ˆ˜ยท์†๋ฐ• ์ŠคํŠธ๋ ˆ์Šค์— ์˜ํ•œ ์œ„ ๊ถค์–‘ ๋ชจ๋ธ ์ฅ์—์„œ ์‹์—ผ ์„ญ์ทจ ์ˆ˜์ค€์ด ๊ถค์–‘ ๋ฐœ๋ณ‘ ๋ฐ ํšŒ๋ณต์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธๅคงๅญธๆ ก ๅคงๅญธ้™ข :้ฃŸๅ“็‡Ÿ้คŠๅญธ็ง‘,1996.Maste

    ๊ณ ์••ํ„ฐ๋นˆ ๋…ธ์ฆ ๋ƒ‰๊ฐ์„ ์œ„ํ•œ ๋ง‰๋ƒ‰๊ฐ ํ™€ ๋ฐฐ์—ด์˜ ๊ฐ•๊ฑด ์ตœ์ ์„ค๊ณ„

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2017. 2. ์ด๊ด€์ค‘.Film hole array optimization has been started and considered recently, despite its various difficulties. With its early stage attention in the research field, there are many issues that should be addressed. One of the significant issues in film hole array optimization is the existence of uncertainty in a high-pressure turbine. Film holes in the 1st stage turbine nozzle work under high uncertainty conditions, the main source of which arises from the manufacturing tolerance and varying flow conditions of the turbine inlet and cooling system. Without consideration of these factors, the optimization results can be ineffective or cause critical failure of the mission if there is any difference between the operating and design condition. In this study, three separate robust design optimization studies for a film cooling hole array are performed under consideration of manufacturing and operational uncertainties. To determine the design variables, the film hole array is parameterized by using newly suggested shape functions with five design variables. The Efficient Design Optimization method coupled with the Kriging model and Monte Carlo simulation, as well as the Genetic Algorithm are used as robust design optimization methods. The manufacturing tolerance and blowing ratio variance of a film hole and the turbine inlet temperature profile are considered as an uncertainty and probabilistic density function, and the variation range of these uncertainties are quantified referring to the open literature by several random variables Thus, film hole arrays showing high cooling performance and high robustness to the uncertainties are successfully obtainedsequentially, the results are compared with each other to derive the following conclusions. Manufacturing tolerance is the most influential followed by variation of the blowing ratio, while the variation of the turbine inlet temperature profile hardly affects the film cooling performance. The region whose temperature fluctuates the most on the nozzle surface appears differently according to the uncertainties, but the random variables related to the holes near the leading edge of the nozzle have a larger impact on the cooling performance than the others regardless of the type of uncertainty.1. Introduction 1 1.1 Film cooling techniques 1 1.2 Uncertainties in High Pressure Turbine 7 1.3 Motivation and scope of the dissertation 10 2. Numerical approach 12 2.1 Fluid and thermal analysis 12 2.1.1 Governing equations 12 2.1.2 Turbulent modeling 13 2.1.3 Treatment of wall 15 2.1.4 Heat transfer calculation 16 2.2 Design optimization method 19 2.2.1 Kriging model 19 2.2.2 Efficient design optimization method 21 2.2.3 Monte Carlo simulation 24 2.2.4 Genetic algorithm 26 3. Reference model 31 3.1 Aerodynamic design of the nozzle 31 3.2 Cooling design of the nozzle 32 3.2.1 Grid and boundary conditions 34 3.3 Simplified cooled nozzle 36 3.3.1 Grid and boundary conditions 37 3.4 Comparison of the results 39 4. Deterministic optimization for the arrangement of film cooling holes 42 4.1 Parameterization for film hole array 42 4.2 Optimization results 47 4.2.1 Problem definition 47 4.2.2 Optimization results 50 5. Uncertainties in the film hole array optimization 80 5.1 Manufacturing tolerance of film hole 80 5.2 Blowing ratio of film hole 85 5.3 Turbine inlet temperature distortion 87 6. Robust design optimization for the arrangement of film cooling holes 93 6.1 Problem definition 93 6.2 RDO considering manufacturing tolerance 97 6.3 RDO considering variance of blowing ratio 101 6.4 RDO considering TIT distortion 105 6.5 Comparison of the results 109 6.5.1 Pareto front 109 6.5.2 Array configuration 110 6.5.3 Probability distribution 111 6.5.4 Film cooling effectiveness 115 7. Conclusion 128 7.1 Summary 128 7.2 Future work 131 Reference 133 ๊ตญ๋ฌธ์ดˆ๋ก 141Docto

    The Issues of Korean Language Education in Cultivating Competent Future Leaders with Multidisciplinary Convergence Skills: Focused on the Using Advertisement in the Teaching-Learning

    No full text
    ์ด ์—ฐ๊ตฌ๋Š” ์œต๋ณตํ•ฉ ๊ต๊ณผ๋กœ์„œ์˜ ๊ตญ์–ด ๊ต๊ณผ์˜ ํŠน์„ฑ์„ ์ค‘์‹ฌ์œผ๋กœ ๋ฏธ๋ž˜ ์‚ฌํšŒ๋ฅผ ์„ ๋„ํ•  ์ˆ˜ ์žˆ๋Š” ์ธ์žฌ ์–‘์„ฑ์„ ์œ„ํ•œ ๊ตญ์–ด๊ต์œก์˜ ๊ณผ์ œ๋ฅผ ๊ณ ์ฐฐํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ ํ˜„๋Œ€์‚ฌํšŒ์—์„œ ์ƒˆ๋กœ์šด ์˜์‚ฌ์†Œํ†ต ๋„๊ตฌ๋กœ ๋ถ€์ƒํ•˜๊ณ  ์žˆ๋Š” ๋งค์ฒด์–ธ์–ด์— ์ฃผ๋ชฉํ•˜๊ณ ์ž ํ•œ๋‹ค. ๊ทธ ์ด์œ ๋Š” ๋งค์ฒด์–ธ์–ด์˜ ๋ณตํ•ฉ ์–‘์‹์ด๋ผ๋Š” ํŠน์„ฑ์€ ๊ตญ์–ด ๊ต๊ณผ์˜ ์œต๋ณตํ•ฉ์  ํŠน์„ฑ๊ณผ ์ƒ๊ด€์„ฑ์ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๊ตญ์–ด๊ต์œก์€ ์ด๋Ÿฐ ๋งค์ฒด์–ธ์–ด๋ฅผ ์ดํ•ด, ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ณตํ•ฉ ์–‘์‹ ๋ฌธ์‹์„ฑ์„ ๊ธฐ๋ฅด๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•ด์•ผ ํ•œ๋‹ค. ๋˜ ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๊ด‘๊ณ  ์–ธ์–ด๋ฅผ ์˜ˆ๋กœ ๋“ค์–ด์„œ ๊ตญ์–ด๊ณผ ๊ตญ์–ด ๊ต์ˆ˜-ํ•™์Šต์˜ ์‹ค์ œ๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ ์ด์œ ๋Š” ๊ด‘๊ณ ๊ฐ€ ๊ตญ์–ด ๊ต๊ณผ์—์„œ ์œต๋ณตํ•ฉ์ ์ธ ๋‚ด์šฉ๊ณผ ์ œ์žฌ๋กœ ํ™œ์šฉ๋˜๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๋Ÿฐ ์ธก๋ฉด์—์„œ ๊ตญ์–ด ๊ต๊ณผ๋Š” ์•ž์œผ๋กœ ์œต๋ณตํ•ฉ์ ์ธ ๋Šฅ๋ ฅ์ด ์ค‘์š”์‹œ๋  ์ˆ˜๋ฐ–์— ์—†๋Š” ๋ฏธ๋ž˜ ์‚ฌํšŒ๋ฅผ ์ด๋Œ์–ด๊ฐˆ ์ธ์žฌ ์–‘์„ฑ์— ๋งŽ์€ ๊ณตํ—Œ์„ ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•จ์„ ์ฃผ์žฅํ•˜์˜€๋‹ค. ์ด ์ ์€๊ตญ์–ด๊ต์œก์˜ ๋„๊ตฌ์ , ์ด๋…์ , ๋ฌธํ™”์  ์†์„ฑ๊ณผ ๊ต์œก ๋‚ด์šฉ์˜ ์œต๋ณตํ•ฉ์  ํŠน์„ฑ๊ณผ๋„ ๋ฌด๊ด€ํ•˜์ง€ ์•Š๋‹ค.The aims of this study has been to examine the task of Korean language education in cultivating competent future leaders centered on the characteristic of Korean language education as a form of convergence education. In order to achieve this goal, the study gives attention to media language which is rising as a new means of communication in modern society. This is because the multimodal characteristic of media language has a correlation to the multidisciplinary aspect of Korean language education. Therefore Korean language education must aim to cultivate multimodal literacy which enables students to comprehend and convey thoughts using multimodal language. Furthermore, this study has observed the use of advertisement as an example in an actual teaching-learning in Korean language education. The reason is because advertisements are utilized as converged multidisciplinary contents and materials in Korean language education. From such a perspective, Korean language education needs to be able to contribute significantly towards fostering competent leaders for a future in which multidisciplinary convergence skills will be considered crucial. This aspect is related to the multidisciplinary characteristic of instrumental, ideological and cultural properties of educational contents of Korean language education
    corecore