University of Macau / Faculty of Arts and Humanities. / Department of Chinese

University of Macau / Faculty of Arts and Humanities. / Department of Chinese

文章討論了潮汕澄海話虛指的第三人稱"伊"的語法特點并進行澄海話、普通話和粤語蒙受結構的對比分析。第一部分描寫了澄海話三種虛指代詞"伊"的相關結構──"乞伊"結構、"佮伊"結構和"V伊R"結構的語法特點。"乞伊"結構和"佮伊"結構各可分為A、B兩類，兩種結構內部分別在動詞的選擇、"伊"的所指和"乞/佮"的隱現上表現出不同。兩種結構之間的主要差別則在於主語的題元角色及"乞/佮"的句法位置高低上。"V伊R"結構是"佮伊"結構的一種條件變體，主要用於表達祈使等語氣。 / 文章第二部分分析了澄海話三種蒙受結構的生成機制。并進一步概括出"伊"的種類:從所指對象題元角色的角度可以分為兩類，一是施事或致事、二是蒙事；從所指對象位置的角度也可以分為兩類，一是所指在句中，二是所指在句外。以及"伊"的句法位置：一是，位於"中間層次"；二是，緊鄰輕動詞。 / 文章第三部分嘗試以澄海話蒙受結構的分析方法為角度和基礎，分別分析普通話蒙受結構"V他O"和粤語蒙受結構"VR佢"。通過三種方言蒙受結構的對比分析，我們看到三種方言類型上的不同，也驗證了劉丹青（2001）、鄧思穎（2006）的分析：澄海閩南話是"最弱的SVO"，動詞傾向於留在原地；粤語是"最強的SVO"，動詞傾向於長距離移位；普通話是"溫和的SVO"，動詞移位但距離不長。 / This thesis discusses the syntax of the Chinese non-referential pronoun 伊 yi ‘it’ in Chenghai Southern Min in three different affective constructions: keyi 乞伊 construction, kaiyi 佮伊 construction and V伊R construction. After examining the syntactic and semantic properties of these three yi affective constructions, we figure out that firstly there are two types of keyi 乞伊 construction and two types kaiyi 佮伊 construction. Secondly keyi 乞伊 construction differs from kaiyi 佮伊 construction in theta-roles of the subject and the position of yi. Thirdly V伊R construction is a conditioned variant of keyi 乞伊 construction. The following part we explore the hierarchical order of these three yi affective constructions, and we employ high and middle applicatives to capture their syntactic and semantic idiosyncrasies. In this part yi is classified into two ways according to the property of the object it refers to. For example, in keyi 乞伊 construction, yi always refers to a causer or a agent, and in kaiyi 佮伊 construction, yi refers to an affectee. In both constructions, yi is situated in a middle position which close to light verbs. The last part of this paper compare the affective constructions of Chenghai Southern Min with affective constructions of Mandarin and Cantonese. The main differences among these three dialects match with the claim proposed by Liu(2001) and Tang(2006): Chenghai Southern Min is the weakest SVO construction in which verbs prefer to stay in situ; Cantonese is the strongest SVO construction in which verbs prefer to take long-distance movement; Mandarin is the moderate SVO construction in which verbs will move but won’t move far. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / 金佳. / Thesis (M.Phil.) Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 87-93). / Abstracts in English and Chinese. / Jin Jia.

This thesis discusses different approaches to machine translation (MT) from Dialectal Arabic (DA) to English. These approaches handle the varying stages of Arabic dialects in terms of types of available resources and amounts of training data. The overall theme of this work revolves around building dialectal resources and MT systems or enriching existing ones using the currently available resources (dialectal or standard) in order to quickly and cheaply scale to more dialects without the need to spend years and millions of dollars to create such resources for every dialect. Unlike Modern Standard Arabic (MSA), DA-English parallel corpora is scarcely available for few dialects only. Dialects differ from each other and from MSA in orthography, morphology, phonology, and to some lesser degree syntax. This means that combining all available parallel data, from dialects and MSA, to train DA-to-English statistical machine translation (SMT) systems might not provide the desired results. Similarly, translating dialectal sentences with an SMT system trained on that dialect only is also challenging due to different factors that affect the sentence word choices against that of the SMT training data. Such factors include the level of dialectness (e.g., code switching to MSA versus dialectal training data), topic (sports versus politics), genre (tweets versus newspaper), script (Arabizi versus Arabic), and timespan of test against training. The work we present utilizes any available Arabic resource such as a preprocessing tool or a parallel corpus, whether MSA or DA, to improve DA-to-English translation and expand to more dialects and sub-dialects. The majority of Arabic dialects have no parallel data to English or to any other foreign language. They also have no preprocessing tools such as normalizers, morphological analyzers, or tokenizers. For such dialects, we present an MSA-pivoting approach where DA sentences are translated to MSA first, then the MSA output is translated to English using the wealth of MSA-English parallel data. Since there is virtually no DA-MSA parallel data to train an SMT system, we build a rule-based DA-to-MSA MT system, ELISSA, that uses morpho-syntactic translation rules along with dialect identification and language modeling components. We also present a rule-based approach to quickly and cheaply build a dialectal morphological analyzer, ADAM, which provides ELISSA with dialectal word analyses. Other Arabic dialects have a relatively small-sized DA-English parallel data amounting to a few million words on the DA side. Some of these dialects have dialect-dependent preprocessing tools that can be used to prepare the DA data for SMT systems. We present techniques to generate synthetic parallel data from the available DA-English and MSA- English data. We use this synthetic data to build statistical and hybrid versions of ELISSA as well as improve our rule-based ELISSA-based MSA-pivoting approach. We evaluate our best MSA-pivoting MT pipeline against three direct SMT baselines trained on these three parallel corpora: DA-English data only, MSA-English data only, and the combination of DA-English and MSA-English data. Furthermore, we leverage the use of these four MT systems (the three baselines along with our MSA-pivoting system) in two system combination approaches that benefit from their strengths while avoiding their weaknesses. Finally, we propose an approach to model dialects from monolingual data and limited DA-English parallel data without the need for any language-dependent preprocessing tools. We learn DA preprocessing rules using word embedding and expectation maximization. We test this approach by building a morphological segmentation system and we evaluate its performance on MT against the state-of-the-art dialectal tokenization tool.

蔡玄暉. / Submitted: Dec. 2009. / Thesis (doctoral)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (p. 371-382). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in Chinese and English. / Cai Xuanhui.

何丹鵬. / "二〇〇九年九月". / "2009 nian 9 yue". / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 355-363). / Abstract in Chinese and English. / He Danpeng. / Chapter 第一章 --- 引言 --- p.1 / Chapter 第一節 --- 研究背景 --- p.1 / Chapter 第二節 --- 龍勝概況 --- p.2 / Chapter 第三節 --- 伶話簡介 --- p.3 / Chapter 第四節 --- 調查情況 --- p.4 / Chapter 第二章 --- 伶話語音糸統 --- p.6 / Chapter 第一節 --- 聲韻調糸統 --- p.6 / Chapter 第二節 --- 聲調調值的分析 --- p.8 / Chapter 第三節 --- 連讀變調 --- p.16 / Chapter 第四節 --- 聲韻調配合關係 --- p.23 / Chapter 第五節 --- 伶話同音字表 --- p.55 / Chapter 第三章 --- 伶話聲母的古今演變與層次 --- p.78 / Chapter 第一節 --- 中古聲母與伶話今讀比較表 --- p.78 / Chapter 第二節 --- 全濁聲母今讀的層次 --- p.80 / Chapter 第三節 --- 幫、非組聲母今讀的層次 --- p.91 / Chapter 第四節 --- 泥'來母今讀的層次 --- p.95 / Chapter 第五節 --- 端、知組聲母今讀的層次 --- p.96 / Chapter 第六節 --- 精、莊、知、章組聲母今讀的層次 --- p.99 / Chapter 第七節 --- 見組聲母今讀的層次 --- p.108 / Chapter 第八節 --- 曉、匣母今讀的層次 --- p.113 / Chapter 第九節 --- 影、喻母今讀的層次 --- p.117 / Chapter 第四章 --- 伶話韻母的古今演變與層次 --- p.122 / Chapter 第一節 --- 中古韻母與伶話今讀比較表 --- p.122 / Chapter 第二節 --- 果、假攝韻母今讀的層次 --- p.125 / Chapter 第三節 --- 純四等韻母今讀的層次 --- p.130 / Chapter 第四節 --- 梗、曾攝韻母今讀的層次 --- p.136 / Chapter 第五節 --- 陽、人聲韻母今讀的層次 --- p.142 / Chapter 第五章 --- 伶話聲調的古今演變與層次 --- p.150 / Chapter 第一節 --- 古平聲今讀的層次 --- p.152 / Chapter 第二節 --- 古上聲今讀的層次 --- p.159 / Chapter 第三節 --- 古去聲今讀的層次 --- p.160 / Chapter 第四節 --- 古人聲今讀的層次 --- p.163 / Chapter 第五節 --- 小結 --- p.164 / Chapter 第六章 --- 結語 --- p.168 / Chapter 附錄一： --- 伶話字音對照表（附苗話、桂林話） --- p.172 / Chapter 附錄二： --- 伶話分類詞表 --- p.281 / Chapter 附錄三： --- 伶話語法例句 --- p.341 / Chapter 附錄四： --- 伶話語料 --- p.351 / 主要參考文獻 --- p.355

Thesis ( M. A. (Translation Studies and Linguistics)) -- University of Limpopo, 2017 / The study explored the restandardization of Sepedi with the aspiration of including Khelobedu dialectical lexicons in the standard form. The standardization of Sepedi, unlike the case of Shona, excluded many of its dialects from the process, thus, left Khelobedu speakers outside of this medium and later subjected them to learn it in schools, putting them at a point of disadvantage academically. Very few studies have been conducted around this term restandardization. This study is mixed method in approach and sequential in design. Data is collected via self-administered questionnaires and face-to-face interviews using an interview guide. A total of 20 participants from four villages in the Mopani District made up a sample for the quantitative data collection phase, while four participants who are Language practitioners by profession made up the qualitative phase of the study. The findings of the study reveal that dialect speakers do not have much confidence in their dialectical variety. They still believe that English and Sepedi are mediums of development and progress. Although restandardization according to the language practitioners is said to possible, PanSALB still has a lot to do in terms of developing Indigenous Languages in South Africa.

Thesis (M.A. (Translation studies and linguistics)) --University of Limpopo, 2009 / This mini-dissertation describess the phonological structure of Tshimanḓa dialect, comparing it with the standard Tshivenḓa. The study shows the historical background of Tshimanḓa dialect and also points out that Tshimanda is a dialect spoken by Vhalaudzi of Lwamondo, Gwamasenga, Tshimbupfe and Luonde in Limpopo Province, South Africa. The study has revealed two aspects which characterise Tshimanda dialect. Tshimanda dialect is characterized by the omission of two speech sounds, namely, ‘l’ and ‘w’. In some instances the omission of the speech sound ‘l’ in Tshimanda dialect gives a word a different meaning than the one in standard Tshivenda.

No description available.

Abstract not available