近年來，品牌的經營與發展是台灣廠商日漸重視的議題，而欲造就一個成功的品牌，必須能照料與品牌相關之內隱屬性與外顯屬性的所有面向。雖然品牌名稱僅是外顯屬性之一，但卻也是產品的一部分，若能為產品取一個好的名稱，不僅能提升產品價值，還能為廠商節省許多行銷費用，因此，好品名的重要性不言而喻。然而，有關品牌名稱的相關文獻資料相當少，且討論的架構也不甚完整，此外，目前既有的文獻也大多著重於探究英文品牌名稱的良窳，較少有專研中文品牌名稱的部分。而中文字與英文字本身在造字、構詞上即有相當大的差異，不能以同樣的準則審視之，因此，本研究將以中文的品牌名稱作為探討主體，深入研究現有品牌名稱的命名原則與其優缺點，而有關優缺點的評估，則以Aaker (1991)與Keller(1998)對於品牌權益的定義來判斷之。爾後，再挑選特定的分類與品項，個別探討不同的分類與品項，是否有別於一般性的命名原則。 由於探討台灣中文品牌命名原則的相關研究相當缺乏，因此本研究為探索性研究，採取次級資料分析法與個案研究法，並以《管理雜誌》2007年與2008年台灣全區消費者心目中理想品牌調查的結果作為分析對象，但因此調查僅列出各品項前三名的品牌，因此筆者透過實體通路與相關網站做搜尋，另加入其他的知名品牌作為分析之標的。 而經過研究分析與歸納後，本研究提出八個命題：命題1、不論在飲料產品類、3C產品類或服務業中，最常使用的品牌名稱皆為兩到三個字的品名，可能是因為其較簡短易記，且容易被學習與進一步的傳播。命題2、相較於3C產品與服務業，飲料品類更常使用「品牌名稱建議性」的命名方式，其中更以「茶類」產品為主。命題3、在飲料類當中，「咖啡」是最常使用外國品牌聯想式之命名方式。命題4、服務業的品名，相較於飲料與3C產品而言，較具有「穩重」、「規矩」的感覺，且常使用具有「吉祥意涵」的字眼。命題5、除了科技產品之外，「優酪乳」品項也常使用「字母數字型」的命名方式。命題6、「數字性命名」的數字本身是否具有實質的數量意義，並不會影響到消費者好記憶與否，但對品牌聯想與知覺品質有影響。命題7、容易發音與否，確實會影響到品牌命名時的選用。命題8、台灣的知名中文品牌名稱，並沒有存在大部分尾音上揚的現象。 本研究歸納了所有的發現並提出以下幾點建議：一、運用「品牌名稱建議性」是最能提升品牌權益與強化品牌名稱效益的命名方式。二、品牌名稱要具獨特性，但同時也必須考量是否有利於品牌權益的提升，與是否容易被消費者記憶與傳播。三、不同的產業與品項具有不同的屬性與特性，因此在一般性的命名原則下，還必須考量個別產業與品項的特質再做定奪。

中文繁簡在字體或電腦編碼上明顯不同之外，在部份詞彙的用法也有不同，這些用法不同的詞彙卻有相同意義的詞彙稱為繁簡體中的等義詞，這些等義詞在雙方文化交流時可能會造成一些障礙，例如人們互相對話、文件書籍翻譯或軟體系統等轉換時容易造成詞義上的誤解，目前均以人工方式來解決不同詞彙的問題，均會費時耗力且易疏漏，若能利用科學的方法讓電腦能自動辨識中文繁簡的等義詞，便能利用辨識出的等義詞給予提示，解決繁簡詞義不同所造成的誤解。 依照實驗設計架構，首先建立電腦類與一般類的繁簡體語料庫，作為辨識的基礎，並建立研究的架構與方法，分為二個階段三種方法，第一階段使用第一種方法，我們先使用N-gram辨識等義詞，評估單一方法是否能有效辨識出等義詞，第二階段使用第二種方法PMI-IR & LC-IR方法與第三種方法Context Vector，評估第二階段的方法是否能將等義詞的辨識能力提高。 根據本研究目的，讓電腦能自動在語料庫中自動辨識中文繁簡等義詞，所以提出了新的辨識架構，用N-gram初步辨識出等義詞，並經由PMI-IR & LC-IR與Context Vector方法提高Precision約0~20%不等。本研究結論是採用不同語言的語料庫，使用N-gram能夠辦識出等義詞，並搭配PMI-IR & LC-IR與Context Vector方法後，可以強化與提昇其等義詞辨識的能力，解決單一方法等義詞辨識能力不足之問題。 / Traditional Chinese and Simplied Chinese are not only different in the typeface and in the computer code, but also in the partial usage of vocabularies. These vocabularies which have different usage but have the same significance are called synonyms. These synonyms will cause some obstacles and misunderstanding in meaning when two parties have cultural exchange, such as during conversation, documents and books translation or softwares system transformation. What we do to solve the problem now is picked them out by manpower, but that will waste a lot of time and strength and easily make errors. If we can use scientific way to let the computer distinguish automatically the synonyms between Traditional Chinese and Simplied Chinese, we will be able to solve such misunderstanding by the hints of the distinguished synonyms. According to the structure of experiment, to let the computer distinguish automatically the synonyms between Traditional Chinese and Simplied Chinese, we have to establish a Traditional Chinese and Simplied Chinese computer category and a general category first as the basis of identification. We should build up the research structure and the method, which divided into two stages and three methods. The first stage uses the first method to use N-gram to distinguish the synonyms and then review if this single method can identify the synonyms effectively. The second stage uses the second method PMI-IR & LC-IR and the third method Context Vector and review if the second stage can raise the synonyms’ ability of identification. According to this research purpose, the computer to study on automatic exact recognition synonyms between traditional and simplified Chinese, so has proposed the new structure of distinguishing, N-gram automatic exact recognition synonym tentatively, and PMI-IR & LC-IR and Context Vector method can improve Precision about 0~20%. This conclusion is a corpus base of using different languages, using N-gram can be exact recognition synonyms, PMI-IR & LC-IR and Context Vector method, can improve single method ability.

數位典藏與數位學習國家型科技計畫14年來透過數位化方式典藏國家文物，例如：生物、考古、地質等15項主題，為了能讓數位典藏資料與時事互動故使用關鍵字作為數位典藏資料與時事的橋樑，由於時事資料會出現新字詞，因此，本研究將提出一個演算法在不使用詞庫或字典的情況下對單一篇中文文章擷取主題關鍵字，此演算法是以Bigram的方式斷詞因此字詞最小單位為二個字，例如：「中文」，隨後挑選出頻率詞並採用分群的方式將頻率詞進行分群最後計算每個字詞的卡方值並產生主題關鍵字，在文章中字詞共現的分佈是很重要的，假設一字詞與所有頻率詞的機率分佈中，此字詞與幾個頻率詞的機率分佈偏差較大，則此字詞極有可能為一關鍵字。在字詞的呈現方面，中文句子裡不像英文句子裡有明顯的分隔符號隔開每一個字詞，造成中文在斷詞處理上產生了極大的問題，與英文比較起來中文斷詞明顯比英文來的複雜許多，在本研究將會比較以Bigram、CKIP和史丹佛中文斷詞器為斷詞的工具，分別進行過濾或不過濾字詞與對頻率詞分群或不分群之步驟，再搭配計算卡方值或詞頻後所得到的主題關鍵字之差異，實驗之資料將採用中央研究院數位典藏資源網的文章，文章的標準答案則來自於中央研究院資訊科學研究所電腦系統與通訊實驗室所開發的撈智網。從實驗結果得知使用Bigram斷詞所得到的主題關鍵字部分和使用CKIP或史丹佛中文斷詞器所得到的主題關鍵字相同，且部分關鍵字與文章主題的關聯性更強，而使用Bigram斷詞的主要優點在於不用詞庫。最後，本研究所提出之演算法是基於能將數位典藏資料推廣出去的前提下所發展，希望未來透過此演算法能從當下熱門話題的文章擷取出主題關鍵字，並透過主題關鍵字連結到相關的數位典藏資料，進而帶動新一波「數典潮」。 / In the past 14 years, Taiwan e-Learning and Digital Archives Program has developed digital archives of organism, archaeology, geology, etc. There are 15 topics in the digital archives. The goal of the work presented in this thesis is to automatically extract keyword s in documents in digital archives, and the techniques developed along with the work can be used to build a connection between digital archives and news articles. Because there are always new words or new uses of words in news articles, in this thesis we propose an algorithm that can automatically extract keywords from a single Chinese document without using a corpus or dictionary. Given a document in Chinese, initially the algorithm uses a bigram-based approach to divide it into bigrams of Chinese characters. Next, the algorithm calculates term frequencies of bigrams and filters out those with low term frequencies. Finally, the algorithm calculates chi-square values to produce keywords that are most related to the topic of the given document. The co-occurrence of words can be used as an indicator for the degree of importance of words. If a term and some frequent terms have similar distributions of co-occurrence, it would probably be a keyword. Unlike English word segmentation which can be done by using word delimiters, Chinese word segmentation has been a challenging task because there are no spaces between characters in Chinese. The proposed algorithm performs Chinese word segmentation by using a bigram-based approach, and we compare the segmented words with those given by CKIP and Stanford Chinese Segmenter. In this thesis, we present comparisons for different settings: One considers whether or not infrequent terms are filtered out, and the other considers whether or not frequent terms are clustered by a clustering algorithm. The dataset used in experiments is downloaded from the Academia Sinica Digital Resources and the ground truth is provided by Gainwisdom, which is developed by Computer Systems and Communication Lab in Academia Sinica. According to the experimental results, some of the segmented words given by the bigram-based approach adopted in the proposed algorithm are the same as those given by CKIP or Stanford Chinese Segmenter, while some of the segmented words given by the bigram-based approach have stronger connections to topics of documents. The main advantage of the bigram-based approach is that it does not require a corpus or dictionary.

日文與中文一樣，皆是使用分類詞的語言。長久以來不乏學者討論日文的量詞，然而，對於界定何者為分類詞、如何區分助數詞、量詞和分類詞等議題，卻缺少統一的定論；一方面是因為語言學上對於分類詞的定義缺乏一定的標準，另一方面，傳統日文文法書中的概念，往往僅用「助數詞」一個詞類就概括了所有數量詞後面的詞類。 本篇論文最主要的目標便是依照一個統一且清楚的定義，來界定日文的分類詞。此次研究先參考了四位語言學者的研究，和四本文法書中的分類詞列表，共整理出前人所列出673個可能的分類詞，之後再透過JpWac和Google Search蒐羅實際語料，對這673個詞逐一進行句法和語意測試，最後界定出其中只有115個是真正的日文分類詞。 在此之後，為瞭解日本人對於名詞的分類和意識，便從這115個分類詞由底層到高層建立一項名詞的分類整理。最後，再由出一份問卷請以日文為母語的日本人填寫使用頻率，初步了解現代日本語中分類詞的使用狀況。結果顯示，僅有27個分類詞堪列為現代日本語中常使用的分類詞，期望這些真正的分類詞能成為日後臺灣在日文教學之參考。 / Japanese is one of the languages that use numeral classifiers, which can be combined with both numerals and nouns. However, Japanese grammar books tend to use “counters” to call all morphemes preceded by numerals, and in linguistic studies, the definition of numeral classifiers is controversial. Therefore, there is no consistent analysis in identifying Japanese classifiers. The goal of this thesis is to identify Japanese classifiers based on one consistent model. Eight previous works from both traditional grammar and linguistic areas were reviewed, and 673 possible classifiers were collected. Each of the 673 possible classifiers is tested to identify true Japanese classifiers. Two corpora, JpWac and Google Search, are used to collect raw data for syntactic and semantic tests. As a result, only 115 true Japanese classifiers are found. After identifying the true classifiers, a bottom-up classification is performed to understand the concept of noun categorization by native Japanese speakers. A questionnaire is created to evaluate the usage frequencies of these true classifiers. Based on the survey, only 27 out of the 115 classifiers are estimated to be frequently used classifiers.

剛出生前幾年，幼兒已逐漸能理解並談論自己和別人的不同。然而，真正習得用來指涉自己和別人的指涉詞不是那麼容易，因為至少得先理解角色轉換概念和說話者當時的說話目的為何。因此，在幼兒學會用大人模式指涉自己和別人前，他們會先有自己一套獨特的語言使用模式。本研究的目的在於探討一位以中文為母語的小孩如何用指涉詞表達自己的語用動機。研究對象是一對中階家庭的母女。語料來自於受試親子在家互動的語言。研究結果顯示幼兒特別容易會用指涉詞表達的語用功能種類不多。在語段層次，幼兒初期較會在和母親協調當下要做的事時，以及在和母親討論故事中虛幻世界時指涉自己；後期則除了在和母親協調當下要做的事時較會指涉自己外，在和母親協調未來要做的事、討論和現在或非現在相關的事時也較會指涉自己。另外，幼兒比較會指涉別人時，則是當她想要轉移母親焦點，或是在和母親討論兩人當下共同注視的目標、故事中虛幻世界、和現在相關的事、或是非現在發生的事時。在語句層次，幼兒則是當她在表達願望、要求母親為她做事、陳述個人想法、陳述她想要執行某動作的目的時較會指涉自己。較會指涉別人時則是當她在吸引母親注意、要求母親為她做事、陳述個人想法、和回答Wh-問句時。整體比較來看，母親特別會用指涉詞表達的語用功能種類比幼兒來得多元化。 / During the first years of life, children come to realize and talk about themselves distinct from others. However, acquisition of self- and other-reference forms is not always so easy because it presupposes at least a grasp of shifting roles, together with concept of the speaker’s communicative intents. Before children match adult-like usages of reference, they temporarily formulate their own linkages between language forms and functions. This paper examined how a Chinese young child formulated her pragmatic moves through self- and other-reference forms. One mother-child dyad from middle socioeconomic class were asked to do what they normally did at home. Results show that only a small set of communicative intents provided the particularly fertile contexts for the child’s self- and other-reference. In the speech interchange tier, the child earlier tended to refer to self in Negotiating the immediate activity(NIA) and Discussing the fantasy world(DFW), but later in Negotiating both the immediate and future activity(NIA, NFA),and Discussing the related-to-present(DRP) and the non-present(DNP). She tended to refer to other in Directing hearer’s attention(DHA), Discussing the joint focus of attention(DJF), the fantasy world(DFW), the related-to-present(DRP), and the non-present(DNP). In the speech act tier, the child tended to refer to self to Express a wish (WS), Request / Propose action for hearer(RS), State a proposition(ST), and State intent to carry out act by speaker(SI). She tended to refer to other to Call the hearer’s attention(CL), Request / Propose action for hearer(RS), State a proposition(ST) and Answer a wh-question by a statement(SA). In contrast, the contexts where the mother referred to self and other were more diversified than those where the child referred to self and other.

《新文藝》由國防部新中國出版社於1962年3月至1983年6月間發行，共發行了256期，歷時久遠。這部雜誌呈現、反映出官方對文藝的見解、訴求以及創作成果，在臺灣文學的發展過程中佔有一席之地。是故，本論文以《新文藝》為研究對象，聚焦於「內容的演變」、「黨國文藝政策」、「與臺灣文學發展的關係」以及「連載傷痕文學的策略」等四個部分進行討論。第二章主要說明雜誌發行時「文藝到軍中去」的時代背景、雜誌發行的因果以及黨國文藝政策，並概略介紹《新文藝》之前的《革命文藝》、《軍中文藝》以及《軍中文摘》等名稱的變化；第三章為內容分期，將《新文藝》分為三個時期：戰鬥文藝時期、純文藝時期以及綜合性文藝時期，分別說明三個時期雜誌內容在排版形式以及作品、專欄的消長情形；第四章將焦點集中在三個部分：第一是雜誌的「文藝政策論述」以及對「鄉土文學論戰」的看法；第二是「外國文學的引介」；第三則是「中國古典文學的引介」，說明內容的變遷；第五章則以小說為主題，討論《新文藝》中小說作品題材如何從戰鬥精神與戰爭描寫、愛情婚姻與家庭鳩葛，隨著臺灣經濟起飛，逐步擴大到經商貿易與奮鬥致富，充分呼應了臺灣社會的發展。此外，289期之後更增加了「大陸小說選」，介紹了36篇中國的「傷痕文學」，凸顯出了鮮明的反共立場與反共策略。過去未曾有論者針對《新文藝》的內容加以討論，而軍隊的文學亦是臺灣文學發展中重要的一環，當中仍有許多可加以探討的材料與問題，值得深入挖掘。

本文旨在探討英語人士在中文否定句的習得過程中，是否呈現系統性發展。本研究以訪問的方式進行語料的收集，以比較性問題引導語者產製否定句型。受訪者中八位在國立政治大學語言視聽中心學習中文，二位為長期居住在台灣之英語語者。十位語者的語言表現將視為一個整體，以Implicational Scaling的方法進行結果分析。 中文最主要的否定詞為「不」與「沒」。中文否定句不同於英文否定句在於中文否定句除必需考量句法結構之因素外，否定詞的選用尚必需考量語意因素。除傳統所認為「沒」乃由「不」所衍生出來，且「沒」適用於變化後的（inflected）動詞外，根據石毓智（1992）的分析，「不」與「沒」的使用，具有清楚的分工，「不」用來否定具「連續量」的字詞（word of continuous quantity），而「沒」用來否定具「離散量」的字詞（word of separate quantity）。此外，否定詞的使用，尚受限於被否定的字詞，其定量（fixed quantity）與非定量（non-fixed quantity）的約束。 就表面結構而言，受訪者總共產製了四十二個句型。將這些句型歸納成深層結構而後進行Implicational Scaling。研究結果顯示，英語人士依一系統性的發展順序習得中文之否定句。同時結果亦顯示學習乃由易到難，由普遍到特殊的發展順序。 / The present study aims to explore the developmental sequences of Chinese negation by adult English speakers. The study collects data by interviewing and uses comparative questions to elicit negative structures. Ten adult English speakers are interviewed in which eight study Chinese in The Language Center in Chengchi University and two work in Taiwan over ten years. It is hypothesized that most learners develop a language in a common sequence of steps even though there are deviations of individual speakers. Therefore, the ten informants are regarded as a whole to explore the developmental sequence of Chinese negation by Implicational Scaling. Two main negators in Mandarin Chinese are bu and mei. Traditionally, it is proposed that the two negators are in complementary distribution. Bu becomes to mei when the following verb is you or as an "inflected verb." Besides, according to Shi (1992), the two negators have definitely different functions: bu is used to negate words of continuous quantity and mei words of separate quantity. In addition, whether a word or a phrase is fixed-quantity one or non-fixed-quantity one also influences the occurrence with the negators. Accordingly, It is obviously that Chinese negative structures differ from English and is much more complicated than English. There are 42 surface structures of negation produced by the informants which are generalized into eighteen ones based on the rules of deep structures. And then they are scaled into an implicational ordering. The results are valid and we can infer the development of negation into several stages. Furthermore, it shows that negative structures which are specifically used in Chinese are acquired latter. Therefore, it is proposed that adult English speakers acquire Chinese negation in a common sequence, developing from general structures to specific ones.

University of Macau / Faculty of Social Sciences and Humanities / Department of Chinese

University of Macau / Faculty of Social Sciences and Humanities / Department of Chinese

University of Macau / Faculty of Social Sciences and Humanities / Department of Chinese