38 research outputs found

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    ์ค‘๊ตญ์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ์–ดํœ˜ ํ•™์Šต์ „๋žต ์š”์ธ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ๊ตญ์–ด๊ต์œก๊ณผ(ํ•œ๊ตญ์–ด๊ต์œก์ „๊ณต), 2020. 8. ๊น€ํ˜ธ์ •.๋ณธ ์—ฐ๊ตฌ๋Š” ์ค‘๊ตญ์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ์–ดํœ˜ ํ•™์Šต์ „๋žต ์š”์ธ์„ ๋ถ„์„ํ•˜์—ฌ ๊ทธ ํŠน์„ฑ์„ ๋ฐํžˆ๊ณ  ์–ดํœ˜ ํ•™์Šต์ „๋žต๊ณผ ํ•™์Šต์ž ์š”์ธ๊ณผ์˜ ๊ด€๊ณ„ ๋ถ„์„์„ ํ†ตํ•ด ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ํšจ๊ณผ์ ์ธ ๊ต์ˆ˜ยทํ•™์Šต ๋ฐฉํ–ฅ์„ ํƒ์ƒ‰ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ๊ทธ ๋™์•ˆ ์–ดํœ˜ ๊ต์ˆ˜๋Š” ํ•„์š”์„ฑ์„ ์ธ์ •๋ฐ›์ง€ ๋ชปํ•˜๊ณ  ํ•™์Šต์ž ๊ฐœ์ธ์˜ ๋ฌธ์ œ๋กœ ์—ฌ๊ฒจ์ ธ ์™”๋‹ค. ์ค‘๊ตญ์ธ ํ•™์Šต์ž๋“ค์€ ์ƒˆ๋กœ์šด ์–ดํœ˜๋ฅผ ์ ‘ํ•  ๋•Œ ์–ดํœ˜ ํ•™์Šต์ „๋žต์„ ํšจ๊ณผ์ ์œผ๋กœ ์‚ฌ์šฉํ•˜์ง€ ๋ชปํ•˜์—ฌ ์™ธ๊ตญ์–ด ํ•™์Šต ์ž์ฒด์— ํฅ๋ฏธ๋ฅผ ์žƒ๊ธฐ๊ฐ€ ์‰ฝ๋‹ค. ์ค‘๊ตญ์ธ ํ•™์Šต์ž๋“ค์ด ํ•œ๊ตญ์–ด ์–ดํœ˜ ํ•™์Šต ์‹œ์— ๋ฌด์ž‘์ • ๋‹จ์–ด๋ฅผ ์•”๊ธฐํ•˜๋Š” ๋ฐ ์ˆ˜๋งŽ์€ ์‹œ๊ฐ„์„ ํˆฌ์žํ•˜์˜€์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์–ดํœ˜์˜ ์ •ํ™•ํ•œ ์“ฐ์ž„์ƒˆ๋ฅผ ํŒŒ์•…ํ•˜์ง€ ๋ชปํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ์ด์— ํ•ด๋‹นํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด์™€ ๊ฐ™์€ ๋ฌธ์ œ์ ์„ ์ธ์‹ํ•˜๊ณ  ์ค‘๊ตญ์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ์ฃผ๋„์ ์ธ ํ•™์Šต์— ์œ ์šฉํ•œ ์–ดํœ˜ ํ•™์Šต์ „๋žต์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋จผ์ €, โ… ์žฅ์—์„œ๋Š” ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ํ•„์š”์„ฑ๊ณผ ์–ดํœ˜ ํ•™์Šต์ „๋žต์— ๊ด€ํ•œ ์ œ2์–ธ์–ด๊ต์œก๊ณผ ํ•œ๊ตญ์–ด๊ต์œก์—์„œ์˜ ์„ ํ–‰์—ฐ๊ตฌ๋ฅผ ๊ฒ€ํ† ํ•˜์˜€๋‹ค. โ…ก์žฅ์—์„œ๋Š” ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋ฅผ ํ† ๋Œ€๋กœ ํ•œ๊ตญ์–ด ์–ดํœ˜์— ๊ด€ํ•œ ๊ฐœ๋…๊ณผ ์–ดํœ˜๊ต์œก์˜ ์œ„์ƒ, ์–ธ์–ด ํ•™์Šต์ „๋žต๊ณผ ์–ดํœ˜ ํ•™์Šต์ „๋žต์— ๊ด€ํ•œ ๊ฐœ๋… ๋ฐ ๋ถ„๋ฅ˜ ์ฒด๊ณ„๋ฅผ ์‚ดํ”ผ๊ณ  ์–ดํœ˜ ํ•™์Šต์ „๋žต์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ํ•™์Šต์ž ์š”์ธ์„ ์„ ์ •ํ•˜์˜€๋‹ค. โ…ข์žฅ์—์„œ๋Š” ์ค‘๊ตญ์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ํŠน์„ฑ์— ๋งž๋Š” ์–ดํœ˜ ํ•™์Šต์ „๋žต ์ธก์ •์„ ์œ„ํ•œ ๋„๊ตฌ๋ฅผ ๊ฐœ๋ฐœํ•˜์—ฌ ์˜๋ฏธ ๋ฐœ๊ฒฌ ์ „๋žต๊ณผ ๊ธฐ์–ต ๊ฐ•ํ™” ์ „๋žต ๋‘ ๊ฐœ ๋ฒ”์ฃผ์™€ ์ƒ์œ„์ „๋žต 6๊ฐœ, ํ•˜์œ„์ „๋žต 61๊ฐœ๋กœ ๊ตฌ์„ฑ๋œ ์„ค๋ฌธ๋ฌธํ•ญ์„ ๋งŒ๋“ค์–ด ์ค‘๊ตญ์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž 577๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค. ์กฐ์‚ฌ ๊ฒฐ๊ณผ๋Š” ์š”์ธ๋ถ„์„(Factor analysis), ๊ธฐ์ˆ  ํ†ต๊ณ„, ๋…๋ฆฝํ‘œ๋ณธ t-๊ฒ€์ •(independent samples t-test), ์ผ์›๋ณ€๋Ÿ‰๋ถ„์„(One-way AVOVA), ์ƒ๊ด€๋ถ„์„(Correlationย analysis), ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„(Multiple regression analysis) ๋“ฑ์˜ ๋ฐฉ๋ฒ•์œผ๋กœ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ โ…ฃ์žฅ์—์„œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ๋จผ์ €, ์–ดํœ˜ ํ•™์Šต์— ๋Œ€ํ•œ ์ธ์‹ ์กฐ์‚ฌ ๊ฒฐ๊ณผ ํ•™์Šต์ž๋“ค์€ ์–ดํœ˜ ํ•™์Šต์˜ ์ค‘์š”์„ฑ๊ณผ ํ•„์š”์„ฑ์„ ํฌ๊ฒŒ ์ธ์‹ํ•˜๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด์–ด์„œ ์š”์ธ๋ถ„์„์„ ํ†ตํ•ด, ์ค‘๊ตญ์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ํŠน์„ฑ์„ ๋ฐํž ์ˆ˜ ์žˆ๋Š” ์˜๋ฏธ ๋ฐœ๊ฒฌ ์ „๋žต๊ณผ ๊ธฐ์–ต ๊ฐ•ํ™” ์ „๋žต ๋‘ ๊ฐœ ๋ฒ”์ฃผ ๋‚ด ์ƒ์œ„์ „๋žต 7๊ฐœ์™€ ํ•˜์œ„์ „๋žต 30๊ฐœ๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ƒ์œ„์ „๋žต์œผ๋กœ๋Š” ์˜๋ฏธ ๋ฐœ๊ฒฌ ์ „๋žต์— ์†ํ•˜๋Š” ์–ดํœ˜ ์˜๋ฏธ ๊ฐ•ํ™” ์ „๋žต๊ณผ ์‚ฌํšŒ์  ์š”์ฒญ ์ „๋žต, ๊ธฐ์–ต ๊ฐ•ํ™” ์ „๋žต์— ์†ํ•˜๋Š” ์—ฐ๊ด€์„ฑ์„ ํ™œ์šฉํ•œ ๊ธฐ์–ต ์ „๋žต, ์ž๊ธฐ ์ฃผ๋„์  ์ธ์ง€ ์กฐ์ ˆ ์ „๋žต, ์„ ํƒ์ ์ธ ๋ฐ˜๋ณต ์ „๋žต, ์–ดํœ˜ ์ •๋ณด ํ™œ์šฉ ์ „๋žต, ์ •์˜์ (์‹ฌ๋ฆฌ์ ) ๋ถˆ์•ˆ ์กฐ์ ˆ ์ „๋žต์˜ 7๊ฐœ ์ƒ์œ„์ „๋žต์œผ๋กœ ๊ฐœ๋…ํ™”๊ฐ€ ๊ฐ€๋Šฅํ•˜์˜€๋‹ค. ์ค‘๊ตญ์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์‚ฌ์šฉ ์–‘์ƒ์„ ๋ฒ”์ฃผ๋ณ„๋กœ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ์˜๋ฏธ ๋ฐœ๊ฒฌ ์ „๋žต์„ ๊ธฐ์–ต ๊ฐ•ํ™” ์ „๋žต๋ณด๋‹ค ๋” ๋งŽ์ด ์„ ํ˜ธํ•˜๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ 7๊ฐœ์˜ ์ƒ์œ„์ „๋žต๋ณ„๋กœ ์‚ดํŽด๋ณด๋ฉด ์–ดํœ˜ ์ •๋ณด ํ™œ์šฉ ์ „๋žต > ์„ ํƒ์ ์ธ ๋ฐ˜๋ณต ์ „๋žต > ์—ฐ๊ด€์„ฑ์„ ํ™œ์šฉํ•œ ๊ธฐ์–ต ์ „๋žต> ์ •์˜์ (์‹ฌ๋ฆฌ์ ) ๋ถˆ์•ˆ ์กฐ์ ˆ ์ „๋žต > ์ž๊ธฐ ์ฃผ๋„์  ์ธ์ง€ ์ „๋žต > ์‚ฌํšŒ์  ์š”์ฒญ ์ „๋žต > ์–ดํœ˜ ์˜๋ฏธ ๊ฐ•ํ™” ์ „๋žต์˜ ์ˆœ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ํ•™์Šต์ž ์š”์ธ์— ๋”ฐ๋ฅธ ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์ฐจ์ด๋ถ„์„์„ ํ•œ ๊ฒฐ๊ณผ ์„ฑ๋ณ„์— ๋”ฐ๋ผ ์—ฌ์„ฑ์ด ๋‚จ์„ฑ์— ๋น„ํ•ด ์ „๋žต์„ ๋” ๋งŽ์ด ์‚ฌ์šฉํ•จ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํ•™์Šต์ž ์—ฐ๋ น์— ๋”ฐ๋ฅด๋ฉด 20๋Œ€ ์ดˆ๋ฐ˜(20~24์„ธ) ํ•™์Šต์ž์™€ 20๋Œ€ ํ›„๋ฐ˜ (25~29์„ธ) ํ•™์Šต์ž ๊ฐ„ ์ฐจ์ด๊ฐ€ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ˆ™๋‹ฌ๋„์— ๋”ฐ๋ฅธ ์ฐจ์ด ๋ถ„์„์—์„œ๋Š” ์ „๋ฐ˜์ ์œผ๋กœ ์ดˆ๊ธ‰, ์ค‘๊ธ‰ ํ•™์Šต์ž๋“ค์ด ๊ณ ๊ธ‰ ํ•™์Šต์ž์— ๋น„ํ•ด ์ „๋žต ํ™œ์šฉ๋„๊ฐ€ ๋†’์•˜๋‹ค. ํ•™์Šต์ž์˜ ์ œ1์™ธ๊ตญ์–ด(์˜์–ด, ํ•œ๊ตญ์–ด)์— ๋”ฐ๋ผ์„œ๋Š” ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๊ณ  ํ•™์Šต ๊ธฐ๊ฐ„์— ๋”ฐ๋ผ 4๋…„ ์ด์ƒ ํ•™์Šต์ž์™€ 1๋…„ ๋ฏธ๋งŒ์ธ ํ•™์Šต์ž ๊ฐ„์— ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ ์—ฐ๋ น, ์ˆ™๋‹ฌ๋„์™€ ์–ดํœ˜ ํ•™์Šต์ „๋žต ๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ์‚ดํŽด ๋ณธ ๊ฒฐ๊ณผ, ์ˆ™๋‹ฌ๋„์™€ ์–ดํœ˜ ํ•™์Šต์ „๋žต ๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋กœ ์ธํ•ด ์ˆ™๋‹ฌ๋„๋ณ„ ํ•˜์œ„์ „๋žต ์‚ฌ์šฉ์˜ ์ˆœ์œ„๋Š” ์–ดํœ˜ ์˜๋ฏธ ๊ฐ•ํ™” ์ „๋žต์˜ ํ•ต์‹ฌ์–ด๋ฅผ ํ™œ์šฉํ•œ ์ „๋žต์ด๋‚˜ ์‚ฌํšŒ์  ์š”์ฒญ ์ „๋žต์˜ ๋ชจ๊ตญ์–ด๋กœ ๋ฒˆ์—ญ์„ ๋ถ€ํƒํ•˜๋Š” ์ „๋žต์ด ์ˆ™๋‹ฌ๋„๊ฐ€ ๋‚ฎ์„์ˆ˜๋ก ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ๋†’์•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์–ดํœ˜ ์ •๋ณด ํ™œ์šฉ ์ „๋žต์— ์†ํ•˜๋Š” ๊ธ€์˜ ๋ฌธ๋งฅ ๋˜๋Š” ์ „ํ›„ ๊ด€๊ณ„๋ฅผ ํ†ตํ•ด ์˜๋ฏธ๋ฅผ ์ถ”์ธกํ•˜๋Š” ์ „๋žต์ด ์ค‘๊ตญ์ธ ํ•œ๊ตญ์–ด ํ•™์Šต์ž์˜ ์ˆ™๋‹ฌ๋„ ํ–ฅ์ƒ์— ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋Š” ์ „๋žต์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ํ•™์Šต์ž์˜ ์ˆ™๋‹ฌ๋„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์–ดํœ˜ ํ•™์Šต์ „๋žต์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์–ดํœ˜ ์ •๋ณด ํ™œ์šฉ ์ „๋žต์˜ ๊ธ€์˜ ๋ฌธ๋งฅ ๋˜๋Š” ์ „ํ›„ ๊ด€๊ณ„๋ฅผ ํ†ตํ•ด ์˜๋ฏธ๋ฅผ ์ถ”์ธกํ•˜๋Š” ์ „๋žต๊ณผ ์ •์˜์ (์‹ฌ๋ฆฌ์ ) ๋ถˆ์•ˆ ์กฐ์ ˆ ์ „๋žต์˜ ์–ด๋–ค ๊ฒฝ์šฐ์— ์–ด๋–ค ์–ดํœ˜๋ฅผ ์‚ฌ์šฉ ํ• ์ง€๋ฅผ ์ž˜ ์•„๋Š” ์ „๋žต์€ ํ•™์Šต์ž์˜ ์ˆ™๋‹ฌ๋„์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์–ดํœ˜ ์˜๋ฏธ ๊ฐ•ํ™” ์ „๋žต์ค‘์—์„œ ๋‹ค๋ฅธ ์‚ฌ๋žŒ์—๊ฒŒ ํ€ด์ฆˆ๋ฅผ ํ†ตํ•ด ์–ดํœ˜๋ฅผ ๊ธฐ์–ตํ•˜๋Š” ์ „๋žต์€ ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ•˜์˜€๋‹ค๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด์ƒ๊ณผ ๊ฐ™์ด, ๋ณธ ์—ฐ๊ตฌ๋Š” ์ค‘๊ตญ์ธ ํ•™์Šต์ž์˜ ํŠน์„ฑ์„ ๋ฐํž ์ˆ˜ ์žˆ๋Š” ์–ดํœ˜ ํ•™์Šต ์ „๋žต์˜ ๋ฒ”์ฃผ์™€ ์„ธ๋ถ€ ์ „๋žต์„ ์ถ”์ถœํ•˜์—ฌ ๊ณ ์ฐฐํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ค‘๊ตญ์ธ ํ•™์Šต์ž๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋Š” ์–ดํœ˜ ํ•™์Šต ๊ต์œก ๋ฐฉ์•ˆ์„ ๊ฐ•๊ตฌํ•˜๋Š” ๋ฐ ์œ ์šฉํ•œ ๊ธฐ์ดˆ ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.The purpose of this study is to analyze the factors of the vocabulary learning strategy of Chinese Korean learners, reveal their characteristics, and explore the effective teaching and learning direction of the vocabulary learning strategy by analyzing the vocabulary learning strategy and its relationship with the learner factor. Until now, vocabulary teaching and its necessity has been largely disregarded and considered a personal problem for learners. However, Chinese learners are likely to lose interest in foreign language learning itself because they do not use vocabulary learning strategies effectively when they encounter new vocabulary. This evident when Chinese learners, after spending a lot of time memorizing Korean vocabulary recklessly, do not know the exact usage of words. Recognizing these problems, this study conducted research on vocabulary learning strategies useful for Chinese Korean language learners. To achieve this, this study first explains the goals and needs of the study, as well as reviewing second language education and prior research in Korean language education in relation to vocabulary learning strategies in Chapter I. Based on the existing research, Chapter II examined the concept of Korean vocabulary, the status of vocabulary education, the concepts and classification systems of language learning strategies in addition to vocabulary learning strategies, and selected learners' factors that affect vocabulary learning strategies. In Chapter III, 577 Chinese Korean language learners were surveyed by developing tools for measuring vocabulary learning strategies tailored to the characteristics of Chinese Korean language learners; creating a questionnaire consisting of two categories of semantic discovery strategies and memory enhancement strategies, six higher-level strategies and 61 lower-level strategies. The results of the survey were analyzed through Factor analysis, descriptive statistics, independent sample T-Test, one-way AVOVA, Correlation analysis, and Multiple regression analysis.ย  The findings of the study were produced in Chapter IV, and are as follows. Firstly, the perception survey of vocabulary learning found that learners were highly aware of the importance and necessity of vocabulary learning. Through the factor analysis assessment, we were able to derive seven higher-level strategies and 30 lower-level strategies in two categories: meaning discovery strategies and memory enhancement strategies tailored to the characteristics of Chinese Korean language learners. These strategies were further categorized into seven strategies: strengthening vocabulary and meaning, social request strategies, memory strategy using association, self-directed cognitive control strategies, selective iterative strategies, vocabulary information utilization strategies, and affective (psychological) anxiety control strategies. After looking at the usage patterns of vocabulary learning strategies of Chinese Korean language learners by category, we can see that they prefer semantic discovery strategies to memory enhancement strategies. Specifically, if you look at each of the seven top strategies, it appears in the order of vocabulary information utilization strategies > selective iterative strategies > memory strategy using association > affective (psychological) anxiety control strategy > self-directed cognitive control strategies > social request strategy > strengthening vocabulary and meaning strategy. In addition, a difference analysis of vocabulary learning strategies based on learner factors showed that women use strategies more than men. According to the Natural Learning Ordinance, there was a significant difference between learners in their early 20s (ages 20 to 24) and learners in their late 20s (ages 25 to 29). Overall, beginner and intermediate learners were more likely to use strategies than advanced learners in the analysis of differences in proficiency. The experience of learning a first foreign language did not show any significant difference, and significant differences could be identified in the period of learning, particularly between learners of more than four years and learners of less than one year. According to the difference analysis results, the correlation between age, proficiency, learning period, and vocabulary learning strategy was significant. As a result, the lower the level of mastery, the higher the correlation between strategies utilizing the core words of the strengthening vocabulary and meaning strategy through the ranking of the use of sub-strategies by mastery, or strategies that ask others to translate vocabulary into Chinese through quizzes and social request strategies. Also, the strategy of guessing the meaning through context, or by analyzing previous parts of text after finding unknown words was identified as a strategy that could help improve the proficiency of Chinese Korean language learners. In addition, an analysis of vocabulary learning strategies affecting learners' proficiency confirmed that strategies to guess meanings through contexts or contexts of vocabulary information utilization strategies, and strategies to know what vocabulary to use through affective (psychological) anxiety control strategy, had a positive impact on learners' proficiency and a strategy to remember vocabulary through quizzes to others in strengthening vocabulary and meaning strategy, did not have a positive impact. To conclude, this study identified and considered the detailed categories of vocabulary learning strategies that could reveal the characteristics of Chinese Korean language learners. These findings may be meaningful in that they can provide basic data useful for the development of lexicon learning teaching methods for Chinese learners.โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ํ•„์š”์„ฑ 1 2. ์„ ํ–‰์—ฐ๊ตฌ 5 2.1. ์ œ2์–ธ์–ด๊ต์œก์—์„œ์˜ ์–ดํœ˜ ํ•™์Šต์ „๋žต 5 2.2. ํ•œ๊ตญ์–ด๊ต์œก์—์„œ์˜ ์–ดํœ˜ ํ•™์Šต์ „๋žต 10 3. ์—ฐ๊ตฌ ๋Œ€์ƒ ๋ฐ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 16 โ…ก. ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 19 1. ์–ดํœ˜์˜ ๊ฐœ๋…๊ณผ ์–ดํœ˜๊ต์œก 19 1.1. ์–ดํœ˜์˜ ๊ฐœ๋… 19 1.2. ์–ดํœ˜๊ต์œก์˜ ์œ„์ƒ 21 2. ์–ดํœ˜๊ต์œก์—์„œ์˜ ์–ดํœ˜ ํ•™์Šต์ „๋žต 24 2.1. ์–ธ์–ด ํ•™์Šต์ „๋žต์˜ ๊ฐœ๋… 24 2.2. ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ๊ฐœ๋… 26 3. ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ๋ถ„๋ฅ˜ 28 3.1. ์˜๋ฏธ ๋ฐœ๊ฒฌ ์ „๋žต 35 3.2. ๊ธฐ์–ต ๊ฐ•ํ™” ์ „๋žต 37 4. ์–ดํœ˜ ํ•™์Šต์ „๋žต์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ํ•™์Šต์ž ์š”์ธ 41 โ…ข. ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ๋ถ„์„ ๋ฐฉ๋ฒ• ๋ฐ ์ ˆ์ฐจ 45 1. ์—ฐ๊ตฌ ๋Œ€์ƒ ๋ฐ ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ• 45 1.1. ์—ฐ๊ตฌ ๋Œ€์ƒ 45 1.2. ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ• 47 2. ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์ธก์ • ๋„๊ตฌ 47 2.1. ๋„๊ตฌ ๊ฐœ๋ฐœ ์ ˆ์ฐจ 47 2.2. ๋„๊ตฌ ๊ฐœ๋ฐœ ๊ณผ์ • 50 3. ์˜ˆ๋น„ ์‹คํ—˜ 62 3.1. ์˜ˆ๋น„ ์‹คํ—˜ ์ฐธ์—ฌ์ž 62 3.2. ์˜ˆ๋น„ ์‹คํ—˜ ์ ˆ์ฐจ ๋ฐ ๊ฒฐ๊ณผ 63 4. ๋ณธ ์‹คํ—˜ 64 4.1. ์‹ ๋ขฐ๋„ ๊ฒ€์ฆ 64 4.2. ํƒ€๋‹น๋„ ๊ฒ€์ฆ 65 5. ์ž๋ฃŒ ๋ถ„์„ ๋ฐฉ๋ฒ• ๋ฐ ์ ˆ์ฐจ 66 โ…ฃ. ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ๋ถ„์„ ๊ฒฐ๊ณผ 68 1. ์–ดํœ˜ ํ•™์Šต์— ๋Œ€ํ•œ ์ธ์‹ ์–‘์ƒ 68 2. ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์š”์ธ๋ถ„์„ 70 3. ์–ดํœ˜ ํ•™์Šต์ „๋žต ์š”์ธ๋ณ„ ์—ฐ๊ด€์„ฑ ๋ถ„์„ 75 4. ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์‚ฌ์šฉ ์–‘์ƒ 80 4.1. ์˜๋ฏธ ๋ฐœ๊ฒฌ ์ „๋žต๋ณ„ ์‚ฌ์šฉ ์–‘์ƒ 87 4.1.1. ์–ดํœ˜ ์ •๋ณด ํ™œ์šฉ ์ „๋žต์˜ ์‚ฌ์šฉ ์–‘์ƒ 87 4.1.2. ์‚ฌํšŒ์  ์š”์ฒญ ์ „๋žต์˜ ์‚ฌ์šฉ ์–‘์ƒ 89 4.2. ๊ธฐ์–ต ๊ฐ•ํ™” ์ „๋žต๋ณ„ ์‚ฌ์šฉ ์–‘์ƒ 90 4.2.1. ์–ดํœ˜ ์˜๋ฏธ ๊ฐ•ํ™” ์ „๋žต์˜ ์‚ฌ์šฉ ์–‘์ƒ 90 4.2.2. ์—ฐ๊ด€์„ฑ์„ ํ™œ์šฉํ•œ ๊ธฐ์–ต ์ „๋žต์˜ ์‚ฌ์šฉ ์–‘์ƒ 92 4.2.3. ์ž๊ธฐ ์ฃผ๋„์  ์ธ์ง€ ์กฐ์ ˆ ์ „๋žต์˜ ์‚ฌ์šฉ ์–‘์ƒ 93 4.2.4. ์„ ํƒ์ ์ธ ๋ฐ˜๋ณต ์ „๋žต์˜ ์‚ฌ์šฉ ์–‘์ƒ 94 4.2.5. ์ •์˜์ (์‹ฌ๋ฆฌ์ ) ๋ถˆ์•ˆ ์กฐ์ ˆ ์ „๋žต์˜ ์‚ฌ์šฉ ์–‘์ƒ 96 5. ํ•™์Šต์ž ์š”์ธ์— ๋”ฐ๋ฅธ ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์ฐจ์ด ๋ถ„์„ 97 5.1. ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์ฐจ์ด 97 5.2. ์—ฐ๋ น์— ๋”ฐ๋ฅธ ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์ฐจ์ด 105 5.3. ์ˆ™๋‹ฌ๋„์— ๋”ฐ๋ฅธ ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์ฐจ์ด 117 5.4. ํ•™์Šต์ž์˜ ์ œ1์™ธ๊ตญ์–ด์— ๋”ฐ๋ฅธ ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์ฐจ์ด 128 5.5. ํ•™์Šต ๊ธฐ๊ฐ„์— ๋”ฐ๋ฅธ ์–ดํœ˜ ํ•™์Šต์ „๋žต์˜ ์ฐจ์ด 130 6. ์–ดํœ˜ ํ•™์Šต์ „๋žต๊ณผ ํ•™์Šต์ž ์š”์ธ์˜ ์ƒ๊ด€๊ด€๊ณ„ 134 7. ๋…ผ์˜ ๋ฐ ๊ต์œก์  ํ•จ์˜ 147 โ…ค. ๊ฒฐ๋ก  150 *์ฐธ๊ณ ๋ฌธํ—Œ 155 *๋ถ€ ๋ก 1 167 *๋ถ€ ๋ก 2 171 *ABSTRACT 177Maste

    p-Type CaFe2O4 semiconductor nanorods controllably synthesized by molten salt method

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    Pure phase, regular shape and well crystallized nanorods of p-type semiconductor CaFe2O4 have been fabricated for the first time by a facile molten salt assisted method, as confirmed by XRD, TEM, SEM and HRTEM. UV-vis diffuse reflectance spectra and Mott-Schottky plots show that the band structure of the CaFe2O4 nanorods is narrower than that of the CaFe2O4 nanoparticles synthesized by conventional method. The enhancement of the visible-light absorption is due to narrowness of the band gap in CaFe2O4 nanorods. The appropriate ratio between the molten salt and the CaFe2O4 precursors plays an important role in inhibiting the growth of the crystals along the (201) plane to give the desired nanorod morphology. This work not only demonstrates that highly pure p-type CaFe2O4 semiconductor with tunable band structure and morphology could be obtained using the molten salt strategy, but also affirms that the bandgap of a semiconductor may be tunable by monitoring the growth of a particular crystal plane. Furthermore, the facile eutectic molten salt method developed in this work may be further extended to fabricate some other semiconductor nanomaterials with a diversity of morphologies. (C) 2016 Science Press and Dalian Institute of Chemical Physics. All rights reserved

    Effect of Dietary Supplemented with Mulberry Leaf Powder on Growth Performance, Serum Metabolites, Antioxidant Property and Intestinal Health of Weaned Piglets

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    Background: The study aimed to explore the effect of mulberry leaf powder (MP) on the performance, serum metabolites and antioxidant property, as well as intestinal health, of weaned piglets. A total of 120 healthy piglets weighing 8.43 ยฑ 1.91 kg (Duroc ร— (Landrace ร— Yorkshire); weaned at 28 d) were chosen and classified into four treatments with three replicates of 10 piglets each based on a randomized complete block design (barrow:gilt = 1:1). The diet treatments were a cornโ€“soybean meal basal diet added with 0% (Ctrl), 2% (MP_2), 4% (MP_4) and 6% MP (MP_6), respectively. The feeding experiment was 28 days in total. The feeding period lasted for 28 days in total. Results: The diet supplemented with 2% MP had no detrimental effects on the growth performance, immunity, enzyme capacity and inflammatory factors, as well as intestinal barrier function. MP_2 is capable of decreasing the levels of serum D-lactic acid and lactate dehydrogenase, enhancing the superoxide dismutase capacity in the liver and diminishing the potential pathogenic bacteria Allisonella in the colon. However, compared with MP_2, MP_6 had unfavorable effects on the average daily gain and average daily feed intake; the concentration of serum non-esterified fatty acids; the activities of superoxide dismutase and glutathione peroxidase and the capacity of lipase and amylase, as well as the intestinal barrier function-related mRNA expression of occludin, claudin-1 and mucin-2 in piglets. Conclusion: Taken together, piglets fed with 2% MP had no adverse effect and was capable of improving the serum metabolites, enhancing the antioxidant capacity (SOD) and lowering the potential pathogenic bacteria of the hindgut (Allisonella). However, the highest concentration of MP (6%) may cause detrimental effects for piglets, which are probably associated with the higher antinutritional factors and fiber. Therefore, the dietary supplementation of 2% MP for piglets may be advisable

    Splice form dependence of ฮฒ-neurexin/neuroligin binding interactions

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    Alternatively spliced ฮฒ-neurexins (ฮฒ-NRXs) and neuroligins (NLs) are thought to have distinct extracellular binding affinities, potentially providing a ฮฒ-NRX/NL synaptic recognition code. We utilized surface plasmon resonance to measure binding affinities between all combinations of alternatively spliced ฮฒ-NRX 1-3 and NL 1-3 ectodomains. Binding was observed for all ฮฒ-NRX/NL pairs. The presence of the NL1 B splice insertion lowers ฮฒ-NRX binding affinity by โˆผ2-fold, while ฮฒ-NRX splice insertion 4 has small effects that do not synergize with NL splicing. New structures of glycosylated ฮฒ-NRXs 1 and 2 containing splice insertion 4 reveal that the insertion forms a new ฮฒ strand that replaces the ฮฒ10 strand, leaving the NL binding site intact. This helps to explain the limited effect of splice insert 4 on NRX/NL binding affinities. These results provide new structural insights and quantitative binding information to help determine whether and how splice isoform choice plays a role in ฮฒ-NRX/NL-mediated synaptic recognition

    Survival outcomes of abdominal radical hysterectomy, laparoscopic radical hysterectomy, robot-assisted radical hysterectomy and vaginal radical hysterectomy approaches for early-stage cervical cancer: a retrospective study

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    Abstract Background This study compared the survival outcomes of abdominal radical hysterectomy (ARH) (Nโ€‰=โ€‰32), laparoscopic radical hysterectomy (LRH) (Nโ€‰=โ€‰61), robot-assisted radical hysterectomy (RRH) (Nโ€‰=โ€‰100) and vaginal radical hysterectomy (VRH) (Nโ€‰=โ€‰45) approaches for early-stage cervical cancer to identify the surgical approach that provides the best survival. Methods Disease-free survival (DFS) and overall survival (OS) were calculated using the Kaplanโ€“Meier method, and survival curves were compared using the log-rank test. Results The volume of intraoperative blood loss was greater in the ARH group than in the LRH group, the RRH group or the VRH group [(712.50โ€‰ยฑโ€‰407.59) vs. (224.43โ€‰ยฑโ€‰191.89), (109.80โ€‰ยฑโ€‰92.98) and (216.67โ€‰ยฑโ€‰176.78) ml, respectively; Pโ€‰<โ€‰0.001]. Total 5-year OS was significantly different among the four groups (ARH, 96.88%; LRH, 82.45%; RRH, 94.18%; VRH, 91.49%; Pโ€‰=โ€‰0.015). However, no significant difference in 5-year DFS was observed among the four groups (ARH, 96.88%; LRH, 81.99%; RRH, 91.38%; VRH, 87.27%; Pโ€‰=โ€‰0.061). Conclusion This retrospective study demonstrated that ARH and RRH achieved higher 5-year OS rates than LRH for early-stage cervical cancer
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