268 research outputs found

    Comparaciรณn de la adecuaciรณn de las traducciones ofrecidas on-line en textos de diversos รกmbitos

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    52 p. : graf.Bibliografรญa: p. 37-38En el mundo de hoy en dรญa existe una cantidad creciente de traducciones generadas de manera automรกtica por herramientas de traducciรณn. Ante este hecho, merece la pena preguntarse por la validez de estos mรฉtodos y si las traducciones que generan son de calidad suficiente para no precisar de la revisiรณn posterior por un traductor. En este Trabajo Fin de Grado se busca evaluar un par de herramientas de traducciรณn automรกtica (Google Translate y OpenNMT) con el uso de los estimadores de calidad mรกs habituales (BLEU, TER, NIST, METEOR, CHRF) en diversos รกmbitos lingรผรญsticos (textos de origen jurรญdico, comercial, literario, foros online y subtรญtulos de pelรญculas). Para ello se toman entre 300 y 500 lรญneas de varios textos en inglรฉs, y se buscan o se elaboran traducciones consideradas correctas, que se emplearรกn como traducciones de referencia. Obtendremos igualmente traducciones automรกticas, a las que denominaremos hipรณtesis o candidatas. Tras un formateado y preprocesado, dichos textos serรกn sometidos a un programa encargado de realizar sucesivamente el cรกlculo de los estimadores. Se representan los resultados en forma de grรกfica y se sacan conclusiones. De esta forma se considerarรก quรฉ textos, debido a sus caracterรญsticas intrรญnsecas, son mรกs adecuados para ser traducidos automรกticamente y cuรกles presentan mรกs dificultades y requieren mayor trabajo de post-ediciรณn.Nowadays there are a growing number of translations which are product not of a human translator, but of a machine. An enormous quantity of words in a variety of languages is spewed from automata when fed with text in a source language. In terms of items translated, the productivity is fabulous, but what about the quality of the generated translations? As anyone can corroborate, in a great many occasions they are in dire need of further human-supervised translation. In this end-of-degree project we aim to evaluate a couple of Machine Translation (MT) tools (GoogleTranslate and OpenNMT) using standard adequacy estimates (BLEU, TER, NIST, METEOR, CHRF) translating texts taken from different linguistic fields (legal, commercial, literary, online forums and film captions). For this purpose, we have extracted around 300 to 500 lines from English source texts considered representative of those categories. When possible, we took official reference translations, but when there was no reference text, we made our own translation. In a similar fashion, we used the aforementioned MTs to obtain the candidate translations whose adequacy will be quantified. After formatting and pre-processing, these texts will be used as input to a program, which will calculate the desired estimators. Numeric results are presented as a bar graph to draw conclusions more easily. Therefore, this will show which texts are more suitable to be translated with MTs, due to their intrinsic features, and which ones prove to be more problematic and require more post-edition effort.Grado en Lenguas Modernas y Traducciรณ

    Machine Translation of English Dialogue into Korean on the Basis of Contextual Information

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    The purpose of this research lies in revealing that contextual information such as information about dialogue participants and social status information must be used in a machine translation of English dialogue into Korean. Unlike a single sentence, a dialogue is the conversation between dialogue participants. Although there is no indication of honorification in English dialogue, such indication always appears in Korean dialogue. This means that depending on the relative order of social status among the people involved in English dialogue, translated Korean dialogue must vary

    ํ•œ๊ตญ์ธ ๊ณ ๋“ฑํ•™์ƒ์˜ ์˜์–ด ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ ํ•™์Šต์—์„œ์˜ ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡ ๊ธฐ๋ฐ˜ ๊ต์ˆ˜์˜ ํšจ๊ณผ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์™ธ๊ตญ์–ด๊ต์œก๊ณผ(์˜์–ด์ „๊ณต), 2022.2. ๊น€๊ธฐํƒ.English adjectival transitive resultative constructions (VtR) are notoriously challenging for Korean L2 English learners due to their syntactic and semantic differences from their L1 counterparts. To deal with such a complex structure, like English adjectival VtR, Korean L2 English learners need instructional interventions, including explicit instructions and corrective feedback on the target structure. Human instructors are virtually incapable of offering adequate corrective feedback, as providing corrective feedback from a human teacher to hundreds of students requires excessive time and effort. To deal with the practicality problems faced by human instructors in providing corrective feedback, numerous artificial intelligence (AI) chatbots have been developed to provide foreign language learners with corrective feedback on par with human teachers. Regrettably, many currently available AI chatbots remain underdeveloped. In addition, no prior research has been conducted to assess the effectiveness of corrective feedback offered by an AI chatbot, a human instructor, or additional explicit instruction via video material. The current study examined the instructional effects of corrective feedback from an AI chatbot on Korean high school studentsโ€™ comprehension and production of adjectival VtR. Also, the current study investigated whether the corrective feedback generated by the AI chatbot enables Korean L2 English learners to expand their constructional repertoire beyond instructed adjectival VtR to uninstructed prepositional VtR. To investigate these issues, text-based Facebook Messenger AI chatbots were developed by the researcher. The effectiveness of the AI chatbotsโ€™ corrective feedback was compared with that of a human instructor and with additional video material. Students were divided into four groups: three instructional groups and one control group. The instructional groups included a chatbot group, a human group, and a video group. All learners in the three instructional groups watched a 5-minute explicit instruction video on the form and meaning pairings of the adjectival VtR in English. After that, learners were divided into three groups based on their preferences for instructional types. The learners volunteered to participate in the instructional procedures with corrective feedback from a text-based AI chatbot, a human instructor, or additional explicit instruction using a 15-minute video. Moreover, they took part in three testing sessions, which included a pretest, an immediate posttest, and a delayed posttest. The control group students were not instructed, and only participated in the three testing sessions. Two tasks were used for each test session: an acceptability judgment task (AJT) and an elicited writing task (EWT). The AJT tested participantsโ€™ comprehension of instructed adjectival VtR and uninstructed prepositional VtR. The EWT examined the correct production of instructed adjectival VtR and uninstructed prepositional VtR. The results of the AJT revealed that the instructional treatment (e.g., corrective feedback from the AI chatbot or a human instructor, or additional explicit instruction from the video material) was marginally more effective at improving the comprehension of adjectival VtR than was the case with the control group. On the other hand, the instructional treatment on the adjectival VtR failed in the generalization to prepositional VtR which was not overtly instructed. In the EWT, the participants in the corrective feedback groups (e.g., the chatbot and human groups) showed a more significant increase in the correct production of the instructed adjectival VtR more so than those in the video and control groups. Furthermore, the chatbot group learners showed significantly higher production of uninstructed prepositional VtR compared to any other group participants. These findings suggest that chatbot-based instruction can help Korean high school L2 English learners comprehend and produce complex linguistic structuresโ€”namely, adjectival and prepositional VtR. Moreover, the current study has major pedagogical implications for principled frameworks for implementing AI chatbot-based instruction in the context of foreign language learning.์˜์–ด ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ(English Adjectival Transitive Resultative Construction)์€ ํ•œ๊ตญ์ธ ์˜์–ด ํ•™์Šต์ž๋“ค์—๊ฒŒ ๋ชจ๊ตญ์–ด์˜ ๋Œ€์‘ ๊ตฌ๋ฌธ์ด ๊ฐ–๋Š” ์˜๋ฏธ ํ†ต์‚ฌ๋ก ์  ์ฐจ์ด๋กœ ์ธํ•ด ํ•™์Šตํ•˜๊ธฐ ๋งค์šฐ ์–ด๋ ค์šด ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์˜์–ด ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ๊ณผ ๊ฐ™์€ ๋ณต์žกํ•œ ๊ตฌ๋ฌธ์„ ํ•™์Šตํ•˜๊ธฐ ์œ„ํ•ด์„œ, ํ•œ๊ตญ์ธ ์˜์–ด ํ•™์Šต์ž๋“ค์—๊ฒŒ๋Š” ๋ชฉํ‘œ ๊ตฌ์กฐ์— ๋Œ€ํ•œ ๋ช…์‹œ์  ๊ต์ˆ˜์™€ ๊ต์ •์  ํ”ผ๋“œ๋ฐฑ์„ ํฌํ•จํ•œ ๊ต์ˆ˜ ์ฒ˜์น˜๊ฐ€ ์š”๊ตฌ๋œ๋‹ค. ์ˆ˜๋ฐฑ ๋ช…์˜ ํ•™์Šต์ž๋“ค์—๊ฒŒ ๊ต์ •์  ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ณผ๋„ํ•œ ์‹œ๊ฐ„๊ณผ ๋…ธ๋ ฅ์ด ์š”๊ตฌ๋˜๊ธฐ ๋•Œ๋ฌธ์—, ์ธ๊ฐ„ ๊ต์‚ฌ๊ฐ€ ์ ์ ˆํ•œ ์–‘์˜ ๊ต์ •์  ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณตํ•œ๋‹ค๋Š” ๊ฒƒ์€ ์‚ฌ์‹ค์ƒ ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ต์ •์  ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณตํ•  ๋•Œ ์ง๋ฉดํ•˜๋Š” ์ด๋Ÿฌํ•œ ์‹ค์šฉ์„ฑ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ์™ธ๊ตญ์–ด ํ•™์Šต์ž๋“ค์—๊ฒŒ ์ธ๊ฐ„ ๊ต์‚ฌ์™€ ์œ ์‚ฌํ•œ ๊ต์ • ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋Š” ์ˆ˜๋งŽ์€ ์ธ๊ณต ์ง€๋Šฅ(AI) ์ฑ—๋ด‡์ด ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ์œ ๊ฐ์Šค๋Ÿฝ๊ฒŒ๋„, ํ˜„์žฌ ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ๋งŽ์€ ์™ธ๊ตญ์–ด ํ•™์Šต์šฉ ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์€ ์•„์ง ์ถฉ๋ถ„ํžˆ ๊ฐœ๋ฐœ๋˜์ง€ ์•Š์€ ์ƒํƒœ์— ๋‚จ์•„์žˆ์œผ๋ฉฐ, ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์˜ ๊ต์ •์  ํ”ผ๋“œ๋ฐฑ์ด ๊ฐ–๋Š” ๊ต์ˆ˜ํšจ๊ณผ๋ฅผ ๋น„๊ต ๋ถ„์„ํ•œ ์—ฐ๊ตฌ๋Š” ํ˜„์žฌ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์€ ์ƒํƒœ๋‹ค. ์ด๋Ÿฌํ•œ ์„ ํ–‰์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์— ์ดˆ์ ์„ ๋‘์–ด, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์˜ ๊ต์ •์  ํ”ผ๋“œ๋ฐฑ์ด ํ•œ๊ตญ ๊ณ ๋“ฑํ•™์ƒ์˜ ์˜์–ด ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ์˜ ์ดํ•ด์™€ ์ƒ์„ฑ์— ๋ฏธ์น˜๋Š” ๊ต์ˆ˜ ํšจ๊ณผ๋ฅผ ์‚ดํŽด๋ณด์•˜๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๊ต์ˆ˜ ํšจ๊ณผ๊ฐ€ ์–ธ์–ด์ ์œผ๋กœ ๊ด€๋ จ๋œ ๋‹ค๋ฅธ ์˜์–ด ๊ตฌ๋ฌธ์˜ ํ•™์Šต์—๋„ ์˜ํ–ฅ์„ ๋ผ์น˜๋Š”์ง€๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ๊ต์‹ค์—์„œ ์ง์ ‘ ๊ฐ€๋ฅด์น˜์ง€ ์•Š์•˜๋˜ ๊ตฌ๋ฌธ์ธ ์˜์–ด ์ „์น˜์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ(English Prepositional Transitive Resultative Construction)์˜ ํ•™์Šต ์–‘์ƒ์„ ์•Œ์•„๋ณด์•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ…์ŠคํŠธ ๋ฉ”์‹œ์ง€ ๊ธฐ๋ฐ˜์˜ ํŽ˜์ด์Šค๋ถ ๋ฉ”์‹ ์ €์—์„œ ๊ตฌ๋™๋˜๋Š” ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์˜ ๊ต์ˆ˜ํšจ๊ณผ ๊ฒ€์ฆ์„ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ์— ์ฐธ์—ฌํ•œ ํ•™์ƒ๋“ค์€ ๋„ค ๊ฐœ์˜ ์ง‘๋‹จ์œผ๋กœ ๊ตฌ๋ถ„๋˜์—ˆ๋‹ค: ์„ธ ๊ฐœ์˜ ๊ต์ˆ˜ ์ง‘๋‹จ์—๋Š” ๊ต์ˆ˜์ฒ˜์น˜๊ฐ€ ์ ์šฉ๋˜์—ˆ๊ณ , ํ•œ ๊ฐœ์˜ ํ†ต์ œ ์ง‘๋‹จ์—์„œ๋Š” ๊ต์ˆ˜์ฒ˜์น˜๊ฐ€ ์ ์šฉ๋˜์ง€ ์•Š์•˜๋‹ค. ๊ต์ˆ˜์ฒ˜์น˜๊ฐ€ ์ ์šฉ๋œ ์„ธ ๊ฐœ์˜ ์ง‘๋‹จ์€ ์ฑ—๋ด‡๊ทธ๋ฃน, ์ธ๊ฐ„๊ทธ๋ฃน, ์˜์ƒ๊ทธ๋ฃน์œผ๋กœ ๋ถ„๋ฅ˜๋˜์—ˆ์œผ๋ฉฐ, ์ด๋“ค์€ ๋ชจ๋‘ ์˜์–ด๋กœ ๋œ ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ์˜ ํ˜•ํƒœ์™€ ์˜๋ฏธ ์Œ์— ๋Œ€ํ•œ 5๋ถ„ ๊ธธ์ด์˜ ํ•™์Šต ๋น„๋””์˜ค๋ฅผ ์‹œ์ฒญํ•จ์œผ๋กœ์จ ๋ช…์‹œ์  ๊ต์ˆ˜ ์ฒ˜์น˜๋ฅผ ๋ฐ›์•˜๋‹ค. ๋˜ํ•œ ๋น„๋””์˜ค๋ฅผ ์‹œ์ฒญํ•œ ํ›„ ์„ธ ๊ทธ๋ฃน์˜ ํ•™์Šต์ž๋“ค์€ ๊ต์žฌ๋ฅผ ํ†ตํ•ด ์ œ๊ณต๋˜๋Š” ์–ธ์–ด์—ฐ์Šต์ž๋ฃŒ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๊ณผ์—…์— ์ฐธ์—ฌํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ์„ธ ์ง‘๋‹จ(์ฑ—๋ด‡๊ทธ๋ฃน, ์ธ๊ฐ„๊ทธ๋ฃน, ์˜์ƒ๊ทธ๋ฃน)์€ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ถ”๊ฐ€์  ๊ต์ˆ˜์ฒ˜์น˜๋ฅผ ๋ฐ›์•˜๋‹ค: ์ฑ—๋ด‡๊ทธ๋ฃน ํ•™์Šต์ž๋“ค์€ ๊ต์žฌ ํ™œ๋™๊ณผ ๊ด€๋ จ๋œ ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡๊ณผ์˜ ๋Œ€ํ™”์— ์ฐธ์—ฌํ•จ์œผ๋กœ์จ ์˜ค๋ฅ˜์— ๋Œ€ํ•œ ๊ต์ •์  ํ”ผ๋“œ๋ฐฑ์„ ๋ฐ›์•˜๋‹ค. ์ธ๊ฐ„๊ทธ๋ฃน ํ•™์Šต์ž๋“ค์€ ๊ต์žฌํ™œ๋™์„ ์™„์ˆ˜ํ•œ ๋‚ด์šฉ์„ ์ธ๊ฐ„ ๊ต์‚ฌ์—๊ฒŒ ์ „์†กํ•˜๊ณ , ์ด์— ๋Œ€ํ•œ ๊ต์ •์  ํ”ผ๋“œ๋ฐฑ์„ ๋ฐ›์•˜๋‹ค. ์˜์ƒ๊ทธ๋ฃน ํ•™์Šต์ž๋“ค์€ ๊ต์žฌํ™œ๋™์„ ์™„์ˆ˜ํ•œ ํ›„ ์ด์— ๋Œ€ํ•œ 15๋ถ„์˜ ์ถ”๊ฐ€์ ์ธ ๋ช…์‹œ์  ๊ต์ˆ˜์ž๋ฃŒ๋ฅผ ์˜์ƒ์œผ๋กœ ์‹œ์ฒญํ•˜์˜€๋‹ค. ํ•™์Šต์ž์˜ ๊ต์ˆ˜ํšจ๊ณผ๋Š” ์‚ฌ์ „์‹œํ—˜, ์‚ฌํ›„์‹œํ—˜ ๋ฐ ์ง€์—ฐ ์‚ฌํ›„์‹œํ—˜์œผ๋กœ ๊ฒ€์ฆ๋˜์—ˆ๋‹ค. ํ•œํŽธ ํ†ต์ œ ์ง‘๋‹จ ํ•™์ƒ๋“ค์€ ๊ต์ˆ˜์ฒ˜์น˜ ์—†์ด ์„ธ ๋ฒˆ์˜ ์‹œํ—˜์—๋งŒ ์ฐธ์—ฌํ•˜์˜€๋‹ค. ์„ธ ์ฐจ๋ก€์˜ ์‹œํ—˜์—์„œ๋Š” ์ˆ˜์šฉ์„ฑํŒ๋‹จ๊ณผ์ œ(AJT)์™€ ์œ ๋„์ž‘๋ฌธ๊ณผ์ œ(EWT)์˜ ๋‘ ๊ฐ€์ง€ ๊ณผ์ œ๊ฐ€ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์ˆ˜์šฉ์„ฑํŒ๋‹จ๊ณผ์ œ๋ฅผ ํ†ตํ•˜์—ฌ, ๊ต์ˆ˜๋œ ์˜์–ด ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ๊ณผ ์ง€์‹œ๋˜์ง€ ์•Š์€ ์˜์–ด ์ „์น˜์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ ๋Œ€ํ•œ ์ฐธ๊ฐ€์ž์˜ ์ดํ•ด๋„๋ฅผ ์ธก์ •ํ•˜์˜€๋‹ค. ์œ ๋„์ž‘๋ฌธ๊ณผ์ œ๋ฅผ ํ†ตํ•˜์—ฌ ๊ต์ˆ˜๋œ ์˜์–ด ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ๊ณผ ์ง€์‹œ๋˜์ง€ ์•Š์€ ์˜์–ด ์ „์น˜์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ์„ ์ฐธ์—ฌ์ž๊ฐ€ ์ •ํ™•ํ•˜๊ฒŒ ์‚ฐ์ถœํ•  ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ์ธก์ •ํ•˜์˜€๋‹ค. ์‹œํ—˜์˜ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์•˜๋‹ค. ์ˆ˜์šฉ์„ฑํŒ๋‹จ๊ณผ์ œ์˜ ๊ฒฝ์šฐ, ๊ต์ˆ˜์ฒ˜์น˜๊ฐ€ ์ ์šฉ๋œ ์„ธ ์ง‘๋‹จ์ด ํ†ต์ œ ์ง‘๋‹จ๋ณด๋‹ค ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ์˜ ์ดํ•ด๋„ ํ–ฅ์ƒ์— ์•ฝ๊ฐ„ ๋” ํšจ๊ณผ์ ์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•˜์ง€๋งŒ ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ์— ๋Œ€ํ•œ ๊ต์ˆ˜์ ์ฒ˜์น˜๋Š” ๊ต์ˆ˜๋˜์ง€ ์•Š์€ ์ „์น˜์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ์œผ๋กœ์˜ ํ•™์Šต์— ์˜ํ–ฅ์„ ์ฃผ์ง€ ๋ชปํ•˜์˜€๋‹ค. ์œ ๋„์ž‘๋ฌธ๊ณผ์ œ์˜ ๊ฒฝ์šฐ, ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์ด๋‚˜ ์ธ๊ฐ„ ๊ต์‚ฌ์— ์˜ํ•ด ์ œ๊ณต๋˜๋Š” ๊ต์ • ํ”ผ๋“œ๋ฐฑ ๊ทธ๋ฃน์˜ ์ฐธ๊ฐ€์ž๊ฐ€ ์˜์ƒ๊ทธ๋ฃน ๋ฐ ํ†ต์ œ์ง‘๋‹จ์˜ ์ฐธ๊ฐ€์ž๋ณด๋‹ค ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ์˜ ์˜ฌ๋ฐ”๋ฅธ ์ƒ์„ฑ์— ๋” ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋“œ๋Ÿฌ๋‚ฌ๋‹ค. ๋™์ผํ•œ ๊ต์ˆ˜ ํšจ๊ณผ๊ฐ€ ์ „์น˜์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ์˜ ํ•™์Šต์—์„œ๋„ ๊ด€์ธก๋˜์–ด, ํ˜•์šฉ์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ์˜ ํ•™์Šต์ด ์ „์น˜์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ์˜ ํ•™์Šต์— ์ผ๋ฐ˜ํ™”๊ฐ€ ์ผ์–ด๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ธ๊ฐ„ ๊ต์‚ฌ๊ฐ€ ์ง๋ฉดํ•ด์•ผ ํ•˜๋Š” ์‹ค์šฉ์„ฑ ๋ฌธ์ œ๋ฅผ ๊ทน๋ณตํ•˜๊ณ , ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡์ด ํ•œ๊ตญ์ธ ๊ณ ๋“ฑํ•™๊ต L2 ์˜์–ด ํ•™์Šต์ž๊ฐ€ ํ˜•์šฉ์‚ฌ ๋ฐ ์ „์น˜์‚ฌ ํƒ€๋™๊ฒฐ๊ณผ๊ตฌ๋ฌธ๊ณผ ๊ฐ™์€ ๋ณต์žกํ•œ ์–ธ์–ด ๊ตฌ์กฐ๋ฅผ ์ดํ•ดํ•˜๊ณ  ์ƒ์„ฑํ•˜๋Š” ๋ฐ์— ์ธ๊ฐ„ ๊ต์‚ฌ์™€ ๋น„๊ฒฌ๋  ์ •๋„๋กœ ๊ต์ •์  ํ”ผ๋“œ๋ฐฑ์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ž„์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋˜ํ•œ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡ ๊ธฐ๋ฐ˜ ์™ธ๊ตญ์–ด ๊ต์œก์˜ ์‹ค์ œ์  ์‚ฌ๋ก€ ๋ฐ ํšจ๊ณผ๋ฅผ ์„ ๋„์ ์œผ๋กœ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค๋Š” ์ ์—์„œ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค.ABSTRACT i TABLE OF CONTENTS iii LIST OF TABLES v LIST OF FIGURES vii CHAPTER 1. INTRODUCTION 1 1.1. Statement of Problems and Objectives 1 1.2. Scope of the Research 6 1.3. Research Questions 9 1.4. Organization of the Dissertation 10 CHAPTER 2. LITERATURE REVIEW 12 2.1. Syntactic and Semantic Analysis of Korean and English Transitive Resultative Constructions 13 2.1.1. Syntactic Analysis of English Transitive Resultative Construction 13 2.1.2. Syntactic Analysis of Korean Transitive Resultative Constructions 25 2.1.3. Semantic Differences in VtR between Korean and English 46 2.1.4. Previous acquisition study on English adjectival and prepositional VtR 54 2.2. Corrective Feedback 59 2.2.1. Definition of Corrective Feedback 59 2.2.2. Types of Corrective Feedback 61 2.2.3. Noticeability in Corrective Feedback 67 2.2.4. Corrective Recast as a Stepwise Corrective Feedback 69 2.3. The AI Chatbot in Foreign Language Learning 72 2.3.1. Non-communicative Intelligent Computer Assisted Language Learning (ICALL) 73 2.3.2. AI Chatbot without Corrective Feedback 79 2.3.3. AI Chatbot with Corrective Feedback 86 2.4. Summary of the Literature Review 92 CHAPTER 3. METHODOLOGY 98 3.1. Participants 98 3.2. Target Structure 102 3.3. Procedure of the Study 106 3.4. Instructional Material Shared by the Experimental Group 107 3.4.1. General Framework of the Instructional Session 108 3.4.2. Instructional Material Shared by Experimental Groups 111 3.5. Group-specific Instructional Treatments: Post-Written Instructional Material Activities on Corrective Feedback from Chatbot, Human, and Additional Explicit Instruction via Video 121 3.5.1. Corrective Feedback from the AI Chatbot 122 3.5.2. Corrective Feedback from a Human Instructor 136 3.5.3. Additional Instruction via Video Material 139 3.6. Test 142 3.6.1. Acceptability Judgment Task (AJT) 144 3.6.2. Elicited Writing Task (EWT) 150 3.7. Statistical Analysis 152 CHAPTER 4. RESULTS AND DISCUSSIONS 154 4.1. Results of Acceptability Judgment Task (AJT) 154 4.1.1. AJT Results of Instructed Adjectival VtR 155 4.1.2. AJT Results of Uninstructed Prepositional VtR 160 4.1.3. Discussion 164 4.2. Results of Elicited Writing Task (EWT) 175 4.2.1. EWT Results for Instructed Adjectival VtR 176 4.2.2. EWT Results of Uninstructed Prepositional VtR 181 4.2.3. Further Analysis 187 4.2.4. Discussion 199 CHAPTER 5. CONCLUSION 205 5.1. Summary of the Findings and Implications 205 5.2. Limitations and Suggestions for Future Research 213 REFERENCES 217 APPENDICES 246 ABSTRACT IN KOREAN 297๋ฐ•

    A study of the translation of sentiment in user-generated text

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    A thesis submitted in partial ful filment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Emotions are biological states of feeling that humans may verbally express to communicate their negative or positive mood, influence others, or even afflict harm. Although emotions such as anger, happiness, affection, or fear are supposedly universal experiences, the lingual realisation of the emotional experience may vary in subtle ways across different languages. For this reason, preserving the original sentiment of the source text has always been a challenging task that draws in a translator's competence and fi nesse. In the professional translation industry, an incorrect translation of the sentiment-carrying lexicon is considered a critical error as it can be either misleading or in some cases harmful since it misses the fundamental aspect of the source text, i.e. the author's sentiment. Since the advent of Neural Machine Translation (NMT), there has been a tremendous improvement in the quality of automatic translation. This has lead to an extensive use of NMT online tools to translate User-Generated Text (UGT) such as reviews, tweets, and social media posts, where the main message is often the author's positive or negative attitude towards an entity. In such scenarios, the process of translating the user's sentiment is entirely automatic with no human intervention, neither for post-editing nor for accuracy checking. However, NMT output still lacks accuracy in some low-resource languages and sometimes makes critical translation errors that may not only distort the sentiment but at times flips the polarity of the source text to its exact opposite. In this thesis, we tackle the translation of sentiment in UGT by NMT systems from two perspectives: analytical and experimental. First, the analytical approach introduces a list of linguistic features that can lead to a mistranslation of ne-grained emotions between different language pairs in the UGT domain. It also presents an error-typology specifi c to Arabic UGT illustrating the main linguistic phenomena that can cause mistranslation of sentiment polarity when translating Arabic UGT into English by NMT systems. Second, the experimental approach attempts to improve the translation of sentiment by addressing some of the linguistic challenges identifi ed in the analysis as causing mistranslation of sentiment both on the word-level and on the sentence-level. On the word-level, we propose a Transformer NMT model trained on a sentiment-oriented vector space model (VSM) of UGT data that is capable of translating the correct sentiment polarity of challenging contronyms. On the sentence-level, we propose a semi-supervised approach to overcome the problem of translating sentiment expressed by dialectical language in UGT data. We take the translation of dialectical Arabic UGT into English as a case study. Our semi-supervised AR-EN NMT model shows improved performance over the online MT Twitter tool in translating dialectical Arabic UGT not only in terms of translation quality but also in the preservation of the sentiment polarity of the source text. The experimental section also presents an empirical method to quantify the notion of sentiment transfer by an MT system and, more concretely, to modify automatic metrics such that its MT ranking comes closer to a human judgement of a poor or good translation of sentiment

    Dyslexia and Second Language Learning. State of the art and future perspectives at Pisa University.

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    In this thesis, I focus my attention on the problems connected with English language learning by dyslexic students, concentrating on the university system and, in particular, on the present situation and on future perspectives at Pisa University. I further described the difficulties caused by the study of a foreign language, in particular English, through the answers given by five students of the University of Pisa to a questionnaire that I gave them to complete. As the answers to my questionnaire prove, English language learning is an extremely challenging task for a dyslexic student. They find it hard to apply the grammar rules, to write and comprehend written texts and to speak in English and exhibit problems in the memorization of lexicon and in the correct pronunciation of foreign words. The main objective of this thesis is to find adequate methods and didactic instruments to let dyslexic students reach a good level in second language learning, focusing the attention, in particular, on the university system

    Anglo-German Rivalry over Telecommunication Networks, 1858-1912

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ตญ์ œ๋Œ€ํ•™์› ๊ตญ์ œํ•™๊ณผ, 2023. 2. ์ด๊ทผ.๊ทธ๋™์•ˆ ๊ณผํ•™๊ธฐ์ˆ ์˜ ๋ฐœ์ „์€ ๊ตญ์ œ์ฒด์ œ์˜ ์ค‘์š”ํ•œ ๋ณ€ํ™”๋งˆ๋‹ค ํฌ๊ฒŒ ๊ธฐ์—ฌํ•ด์™”๋‹ค. ๊ณผํ•™๊ธฐ์ˆ  ๋ถ„์•ผ์—์„œ์˜ ์šฐ์—ด์€ ํ•œ ๊ตญ๊ฐ€์˜ ์„ฑ์žฅ๊ณผ ๊ตญ์ œ์ฒด์ œ ๋‚ด ์œ„์ƒ์„ ๊ฒฐ์ •ํ•˜๋Š” ์ฃผ์š” ์š”์ธ์œผ๋กœ, ๊ตญ์ œ์ •์น˜์˜ ์ฃผ์š” ๊ฐœ๋…์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋Š” ๊ถŒ๋ ฅ(power)๊ณผ ์•ˆ๋ณด(security)์— ๋ฐ€์ ‘ํ•˜๊ฒŒ ์—ฐ๊ณ„๋˜์–ด ์ž‘์šฉํ•ด์™”๋‹ค. ๊ทธ ์ค‘์—์„œ๋„ ๊ตญ๊ฐ€ ๊ฐ„ ์—ฐ๊ฒฐ์„ฑ์— ๊นŠ์ด ๊ด€์—ฌํ•˜๋Š” ๊ธฐ์ˆ ์ผ์ˆ˜๋ก ๊ตญ์ œ์ •์น˜์ ์œผ๋กœ ์ „๋žต์  ๊ฐ€์น˜๊ฐ€ ํฌ๊ธฐ ๋•Œ๋ฌธ์— ์ด๋ฅผ ์ฃผ๋„ํ•˜๊ณ ์ž ํ•˜๋Š” ๊ฐ•๋Œ€๊ตญ๊ฐ„ ๊ฒฝ์Ÿ์ด ์‹ฌํ™”๋˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋Š”๋ฐ ์ •๋ณดํ†ต์‹ ๊ธฐ์ˆ  ๋ถ„์•ผ๊ฐ€ ๋Œ€ํ‘œ์ ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๊ตญ์ œ์ •์น˜ํ•™๊ณ„์—์„œ๋„ ํ•ด๋‹น๊ธฐ์ˆ ์˜ ๊ถŒ๋ ฅ์˜ ๋„๊ตฌ๋กœ์„œ์˜ ์ค‘์š”์„ฑ์ด ์ค‘์ ์ ์œผ๋กœ ๋…ผ์˜๋˜์–ด์™”๊ณ , ํŠนํžˆ ์ฒจ๋‹จ ๊ณผํ•™๊ธฐ์ˆ ์˜ ๋ฐœ์ „๊ณผ ์ตœ๊ทผ ๋ฏธ์ค‘๊ฐ„ ๊ธฐ์ˆ ํŒจ๊ถŒ๊ฒฝ์Ÿ์˜ ๊ฒฉํ™”๋กœ ์ธํ•ด ์ด์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ์ฆ๋Œ€๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ทธ๋™์•ˆ ๊ตญ์ œ์ •์น˜ํ•™์—์„œ ์ •๋ณดํ†ต์‹ ๊ธฐ์ˆ ์€ ์ฃผ๋กœ ์™ธ์žฌ์ ์ธ ์š”์ธ์œผ๋กœ ๊ฐ„์ฃผ๋˜์–ด ๋„๊ตฌ์ ์ธ ์‹œ๊ฐ์—์„œ ๋…ผ์˜๋˜์–ด ์™”์œผ๋ฉฐ, ์ด๋ฅผ ๊ตญ์ œ์ •์น˜์˜ ์ฃผ์š” ๋ณ€์ˆ˜๋กœ ๋‹ค๋ฃฌ ์—ฐ๊ตฌ๋Š” ๋งŽ์ง€ ์•Š์•˜๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตญ์ œ์ •์น˜์—์„œ ๋‹จ์ˆœํžˆ ๊ถŒ๋ ฅ์˜ ๊ตฌ์„ฑ์š”์†Œ๋กœ ๊ธฐ์ˆ ์— ์ ‘๊ทผํ•˜๋Š” ์‹œ๊ฐ์—์„œ ๋‚˜์•„๊ฐ€, ๊ตญ์ œ์ •์น˜์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ๋ ฅ์„ ๋ฏธ์น˜๋Š” ์ฃผ์š” ๋ณ€์ˆ˜๋กœ์„œ์˜ ๊ธฐ์ˆ ์— ์ฃผ๋ชฉํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋„คํŠธ์›Œํฌ ๊ธฐ์ˆ ์ด ๊ตญ๊ฐ€ ๊ฐ„ ๊ด€๊ณ„์™€ ๊ตญ๊ฐ€์˜ ์™ธ๊ต์ •์ฑ…์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ธฐ ์‹œ์ž‘ํ•œ ์ฒซ๋ฒˆ์งธ ์‹œ๊ธฐ์ธ 19์„ธ๊ธฐ ํ›„๋ฐ˜๊ณผ 20์„ธ๊ธฐ ์ดˆ ํ†ต์‹ ๊ธฐ์ˆ  (ํ•ด์ €์ผ€์ด๋ธ”๊ณผ ๋ฌด์„ ์ „์‹ ) ๋„คํŠธ์›Œํฌ ๊ตฌ์ถ• ๋ฐ ๋…์ ์„ ๋‘˜๋Ÿฌ์‹ผ ์˜๊ตญ๊ณผ ๋…์ผ ๊ฐ„์˜ ๊ฒฝ์Ÿ๊ตฌ๋„์— ๋Œ€ํ•œ ์‚ฌ๋ก€์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•œ๋‹ค. ์ด ์‹œ๊ธฐ ์˜๊ตญ๊ณผ ๋…์ผ์˜ ๊ฒฝ์Ÿ๊ตฌ๋„์— ๋Œ€ํ•œ ๊ธฐ์กด์˜ ๋…ผ์˜๋Š” ๊ธฐ์ˆ ์„ ์ฃผ์š” ์š”์†Œ๋กœ ๊ณ ๋ คํ•˜๋Š” ์ ‘๊ทผ์ด ๊ฒฐ์—ฌ๋˜์–ด ์žˆ์—ˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ทธ ์„ค๋ช…์— ํ•œ๊ณ„๋ฅผ ๋…ธ์ •ํ•˜๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตญ์ œ์ •์น˜ํ•™์˜ ์ฃผ์š”์ด๋ก ์ธ ์„ธ๋ ฅ๊ท ํ˜•์ด๋ก ์„ ํ† ๋Œ€๋กœ ๋„คํŠธ์›Œํฌ ๊ธฐ์ˆ ์˜ ๋‚ด์žฌ์ ์ธ ํŠน์„ฑ์ธ ๋„คํŠธ์›Œํฌ ํšจ๊ณผ๋ฅผ ์ฃผ์š” ์„ค๋ช…๋ณ€์ˆ˜๋กœ ๋„์ž…ํ•˜์—ฌ ์ƒˆ๋กœ์šด ๋ถ„์„ํ‹€์ธ ๋„คํŠธ์›Œํฌ ๊ท ํ˜• ๋ชจ๋ธ์„ ์ œ์‹œํ•˜๊ณ  ์ด๋ฅผ ํ•ด๋‹น์‚ฌ๋ก€ ๋ถ„์„์— ๋„์ž…ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋„คํŠธ์›Œํฌ ํšจ๊ณผ๊ฐ€ ๋‚ด์žฌ๋œ ๊ธฐ์ˆ ์ด ๊ฐ–๋Š” ๊ตญ์ œ์ •์น˜์  ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐํ•˜๊ณ , ํ•ด๋‹น๊ธฐ์ˆ ๋กœ ํ˜•์„ฑ๋œ ๋„คํŠธ์›Œํฌ ๋‚ด์—์„œ ๊ตญ๊ฐ€๊ฐ„ ๋„คํŠธ์›Œํฌ ํšจ๊ณผ๊ฐ€ ๋ฐœ์ƒํ–ˆ์„ ๋•Œ ์ดˆ๋ž˜๋˜๋Š” ์ง€์†์ ์ธ ๋น„๋Œ€์นญ์  ๊ด€๊ณ„ํ˜•์„ฑ ๋ฐ ์ฃผ๊ถŒ์ œ์•ฝ๊ณผ ์ด์— ๋Œ€์ฒ˜ํ•˜๊ธฐ ์œ„ํ•œ ๊ตญ๊ฐ€์ „๋žต์— ๋Œ€ํ•œ ์„ค๋ช…์„ ์ œ์‹œํ•œ๋‹ค.The development of technology has accompanied every crucial transformation of the international system as a decisive factor closely linked to the principle concepts of international relations such as power and security. Among different types of technologies, those that enhance connectivity among states are the ones with the strongest implications on international politics as they can reap disproportionate benefits for certain industries of certain countries, generating possibilities for violation of sovereignty that can evoke serious security concerns. In this regard, history has shown that telecommunication technologies, sitting at the core of enabling such connectivity, have often taken an important part in great power competition with their political history paralleling and amplifying trends in international relations. However, while the importance of such technologies has been recognized for their impact on the contours of world politics in existing studies, their conceptualization within the discipline has remained quite limited; they are mostly taken as an exogenous factorโ€”an environmental condition or set of instrumental possibilities, rather than something integral to how international politics are carried out. The lack of clear conceptual and analytical frameworks with which to investigate how technology is developed and implemented, why it is developed and implemented in certain ways, and how these processes impact the order of international politics, makes it difficult to incorporate technology as a core component of international relations discussions. Against this backdrop, this study takes a heuristic approach to show the link between network technology and the balancing strategies taken by great powers. In order to do so, it introduces a new analytical framework, the network balancing model, by incorporating the network effect, an intrinsic property of network technologies, as a key explanatory variable into the balance-of-power theory, in an attempt to show that, theoretically and empirically, the network effect influences balance-of-power politics in ways that have not been appreciated by extant literature in the field of international relations. The model is then applied to analyze the very first case of network effect taking place among the states connected within transnational telecommunication networks and the consequent great power rivalry over the dominance of those networksโ€”the Anglo-German rivalry in the first period of globalization.Chapter 1. Introduction 1 1. A Historical Preview 4 2. Empirical Puzzle and Research Question 7 3. Summary of the Research 9 4. Significance of the Research 14 5. Overview of the Chapters 15 Chapter 2. Literature Review 18 1. Theories of Balance of Power 24 2. Technology in International Relations 39 3. Balance-of-Power and Telecommunication Networks 50 Chapter 3. Analytical Framework and Research Design 72 1. A New Analytical Framework: A Model of Network Balancing 72 2. Propositions 91 3. Research Design 95 Chapter 4. Anglo-German Rivalry over the Submarine Cable Network 103 1. The British Monopoly 104 2. Network Balancing by Germany 133 3. Findings and Analysis 146 Chapter 5. Anglo-German Rivalry over the Wireless Telegraph Network 155 1. Great Britains Embryonic Dominance 155 2. Network Balancing by Germany 165 3. Findings and Analysis 191 Chapter 6. Conclusion 197 1. Findings and Evaluation 198 2. Implications for International Relations Theory 208 3. Implications for the U.S.-China rivalry over ICT networks 210 4. Closing Thoughts 216 References 219 Abstract in Korean 257 Acknowledgments 259๋ฐ•

    Literacies of Bilingual Youth: A Profile of Bilingual Academic, Social, and TXT Literacies

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    This dissertation identifies three types of language skills that urban Spanish/English bilingual youth possess (academic, social, and texting language), and reports on their relationship while documenting and analyzing the features of text messaging among this population. The participants in this study are Spanish-dominant bilingual young adults enrolled in a high school completion program in New York City. They are in the process of developing both Spanish and English academic literacy skills, and it is well known that they tend to perform below the grade they are enrolled in. For this reason, they are often referred to as being โ€œlanguage-lessโ€ (DeCapua & Marshall, 2011; Freeman, Freeman, & Mercuri, 2002) in an academic setting. Yet, little was previously known about their linguistic skills in other language forms such as social and Txt. This research seeks to understand and document their abilities across language forms and modalities, painting a composite picture of non-traditional bilinguals studentsโ€™ linguistic skills. The aims of this dissertation are achieved through three different approaches. The first is a quantitative study into participantsโ€™ literacy skills through the use of assessments measuring academic literacy and social language awareness across written, aural, and digital modalities. The second is an in-depth analysis of the features participants use when texting (communicating via SMS and iMessage). Txt is a relatively new language form, and the analysis presented in this dissertation identifies the features and patterns that illustrate its systematic and constrained nature. The third approach is a case study focused on the texting behavior between two prolific texters. The theories developed based on the texting patterns of all participants (except those two texters) are applied to this one conversation for validation. This conversation constitutes more than half of the text messages that students contributed to the project, highlighting just how important this language form is in the daily life of young adults. A final component of this dissertation is the public availability of the text messages as an anonymized corpus along with the code and methods used to analyze the data. The text message corpus is available at www.byts.commons.gc.cuny.ed

    Evaluation of an Esperanto-Based Interlingua Multilingual Survey Form Machine Translation Mechanism Incorporating a Sublanguage Translation Methodolgy

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    Translation costs restrict the preparation of medical survey and other questionnaires for migrant communities in Western Australia. This restriction is compounded by a lack of affordable and accurate machine translation mechanisms. This research investigated and evaluated combined strategies intended to provide an efficacious and affordable machine translator by: โ€ข using an interlingua or pivot-language that requires less resources for its construction than contemporary systems and has the additional benefit of significant error reduction; and โ€ข defining smaller lexical environments to restrict data, thereby reducing the complexity of translation rules and enhancing correct semantic transfer between natural languages. This research focussed on producing a prototype machine translation mechanism that would accept questionnaire texts as discrete questions and suggested answers from which a respondent may select. The prototype was designed to accept non-ambiguous English as the source language, translate it to a pivot-language or interlingua, Esperanto, and thence to a selected target language, French. Subsequently, a reverse path of translation from the target language back to the source language enabled validation of minimal or zero change in both syntax and semantics of the original input. Jade, an object-oriented (00) database application, hosting the relationship between the natural languages and the interlingua, was used to facilitate the accurate transfer of meaning between the natural languages. Translation, interpretation and validation of sample texts was undertaken by linguists qualified in English, French and Esperanto. Translation output from the prototype model was compared, again with assistance from linguists, with a \u27control\u27 model, the SYSTRAN On-Line Translator, a more traditional transfer translation product. Successful completion of this research constitutes a step towards an increased availability of low cost machine translation to assist in the development of reliable and efficient survey translation systems for use in specific user environments. These environments include, but arc not exclusive to, medical, hospital and Australian indigenous-contact environments

    Journalistic Practice and the Cultural Valuation of New Media: Topicality, Objectivity, Network

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    Around the turn of the twenty-first century, American journalism is undergoing an existential crisis provoked by the emergence of digital and networked communication. As the economic model of producing journalism is undergoing significant changes, this study argues that the crisis of journalism is primarily a cultural crisis of valuation. Because the practices that traditionally defined the exclusivity of journalism as a form of public communication have been transposed to the online and digital environment through social media and blogs, such practices no longer value journalism in the same terms like in the age of mass media. The key to understanding the cultural crisis of journalism in the present, this study argues, is to revise the traditional narrative and its associated terminologies of the institutionalization of journalism. Journalism is thus defined as a structure of public communication, which needs to be enacted by producers and audiences alike to become socially meaningful. The consequence of seeing journalism as a structure sustained through social practices is that it allows to see the relation between audiences and their journalistic media as constitutive for the social function of new media in journalism. Through the analytically central dimension of practice, the study presents key moments in the history of modern journalism, where the meaning of new media was negotiated. These moments include the emergence of topical news media oriented toward a mass market (the penny press in the 1830s) and the definition of a schema of objectivity which valued journalistic practice in professional and scientific terms around the turn of the twentieth century in analogy to photographic media. In each phase, material, cognitive and social practices helped to define the value of a given new medium for journalism. Through the schemas of topicality and objectivity, journalistic practice institutionalized a privileged structure of public communication. The legacy of defining these schemas is then regarded as the central reason for the cultural crisis of journalistic practice in the present, as practices have been transposed and re-valued to sustain either forms of alternative journalism (as peer-production) or forms of self-communication in network media like blogs. Neither the form nor the technology of the blog alone can explain this differential social relevance but only the different ways in which social practices integrated and value new media. The study synthesizes an interdisciplinary array of concepts from cultural studies, sociology and journalism studies on subjects such as public communication, interaction, news production and cultural innovation. The theoretical framework of practice theories is then applied to an extensive body of primary and secondary source material, in order to retrace the cultural valuation of new media in a historically-comparative perspective. The study offers a theoretical and empirical contribution to the analysis of cultural innovation, which can be adopted to other cultural forms and media
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