A catalogue record for this book is available from the British Library ISBN 0 521 82347 1 hardback ISBN 0 521 53032 6 paperback Contents List of tables page vii Preface to the second edition ix Preface to the first edition xii 1 Why a global language? 1 What is a global language? 3 What makes a global language? 7 Why do we need a global language? 11 What are the dangers of a global language? 14 Could anything stop a global language? 25 A critical era 27 2 Why English? The historical context 29 Origins 30 America 31 Canada 36 The Caribbean 39 Australia and New Zealand 40 South Africa 43 South Asia 46 Former colonial Africa 49 Southeast Asia and the South Pacific 54 A world view 59 v Contents

English as a global language

中國科技大學通識教育中心英語文證照奬勵金實施要點 中華民國 105 年 1 月 8 日通識教育委員會議通過 一、 中國科技大學(以下簡稱本校)為鼓勵本校學生通過具公信力機構之英語文能力測驗或 取得證照,特訂定「中國科技大學通識教育中心英語文證照獎勵金實施要點」(以下簡 稱本要點)。 二、 學生於就讀本校期間,通過歐盟共同架構(CEFR)語言能力參考指標 B1(中級)同等級英 語文能力測驗以上(含)者,得依據本要點酌予獎勵。檢測項目請參閱本中心「歐洲語言 學習、教學、評量共同參考架構與各英語檢測分級對照表」(參見附表);未列於標準 對照表之測驗項目不給予獎助。 三、 凡本校學生,除應英系外,均得申請。大學部學生通過同等級以申請一次為限,在學期 間得重複申請,但該次申請之級別不得低於前次。 本獎勵金每學期核發乙次,每次核發全校前 10 名,各名次核發金額如附表。 四、 申請人應提供在學期間,申請當(學)期參加考試之證明文件及成績證明或證照,以憑辦 理。 五、 獎勵金申請作業:請至通識教育中心網頁下載「英語文證照獎勵金申請表」(附件 1), 填妥後檢附成績單正本及影本(背面簽名並註明與正本無異)各一份、本人金融帳戶存 簿(郵局或土地銀行)封面影本送至通識教育中心。 通識教育中心得每學期遴選受獎代表,擇期公開頒奬,並辦理後續請款作業。 六、 奬勵金申請期限:通過相關證照考試半年內應提出申請,逾期視同放棄。 七、 本要點之獎勵金由學校開設通識教育中心專戶,一切收支專款專用;每年度如有剩餘 款,則移至翌年度繼續使用。 八、 本要點經通識教育中心會議審查通過,陳請校長核定後公告實施,修訂時亦同。

文化と言語の多様性の中のCommon European Framework of Reference for Languages: Learning, teaching, assessment (CEFR)--それは基準か? (第10回明海大学大学院応用言語学研究科セミナー 講演)

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Learning Vocabulary In Another Language

Acknowledgements Introduction PART ONE - BACKGROUND Introduction 1. Second language acquisition research: an overview PART TWO - THE DESCRIPTION OF LEARNER LANGUAGE Introduction 2. Learner errors and error analysis 3. Developmental patterns: order and sequence in second language acquisition 4. Variability in learner language 5. Pragmatic aspects of learner language PART THREE - EXPLAINING SECOND LANGUAGE ACQUISITION: EXTERNAL FACTORS Introduction 6. Social factors and second language acquisition 7. Input and interaction and second language acquisition PART FOUR - EXPLAINING SECOND LANGUAGE ACQUISITION: INTERNAL FACTORS Introduction 8. Language transfer 9. Cognitive accounts of second language acquisition 10. Linguistic universals and second language acquisition PART FIVE - EXPLAINING INDIVIDUAL DIFFERENCES IN SECOND LANGUAGE ACQUISITION Introduction 11. Individual learner differences 12. Learning strategies PART SIX - CLASSROOM SECOND LANGUAGE ACQUISITION Introduction 13. Classroom interaction and second language acquisition 14. Formal instruction and second language acquisition PART SEVEN - CONCLUSION Introduction 15. Data, theory, and applications in second language acquisition research Glossary Bibliography Author index Subject index

第二语言习得研究 = The study of second language acquisition

Introduction to Theoretical Linguistics.

[We would like to thank] Cambridge English Language Assessment, a division of Cambridge Assessment, for supporting this research.

/pdf/grammatical-error-correction-using-hybrid-systems-and-type-1mbez0648q.pdf

Grammatical error correction using hybrid systems and type filtering

This paper addresses the task of readability assessment for the texts aimed at second language (L2) learners. One of the major challenges in this task is the lack of significantly sized level-annotated data. For the present work, we collected a dataset of CEFR-graded texts tailored for learners of English as an L2 and investigated text readability assessment for both native and L2 learners. We applied a generalization method to adapt models trained on larger native corpora to estimate text readability for learners, and explored domain adaptation and self-learning techniques to make use of the native data to improve system performance on the limited L2 data. In our experiments, the best performing model for readability on learner texts achieves an accuracy of 0.797 and PCC of $0.938$.

/pdf/text-readability-assessment-for-second-language-learners-kmk1698qbx.pdf

Text Readability Assessment for Second Language Learners

Online social networks (OSNs) have seen a remarkable rise in the presence of surreptitious automated accounts. Massive human user-base and business-supportive operating model of social networks (such as Twitter) facilitates the creation of automated agents. In this paper we outline a systematic methodology and train a classifier to categorise Twitter accounts into ‘automated’ and ‘human’ users. To improve classification accuracy we employ a set of novel steps. First, we divide the dataset into four popularity bands to compensate for differences in types of accounts. Second, we create a large ground truth dataset using human annotations and extract relevant features from raw tweets. To judge accuracy of the procedure we calculate agreement among human annotators as well as with a bot detection research tool. We then apply a Random Forests classifier that achieves an accuracy close to human agreement. Finally, as a concluding step we perform tests to measure the efficacy of our results.

/pdf/classification-of-twitter-accounts-into-automated-agents-and-2kz6kvlgho.pdf

Classification of Twitter Accounts into Automated Agents and Human Users

This paper presents the winning systems we submitted to the Complex Word Identification Shared Task 2018. We describe our best performing systems’ implementations and discuss our key findings from this research. Our best-performing systems achieve an F1 score of 0.8792 on the NEWS, 0.8430 on the WIKINEWS and 0.8115 on the WIKIPEDIA test sets in the monolingual English binary classification track, and a mean absolute error of 0.0558 on the NEWS, 0.0674 on the WIKINEWS and 0.0739 on the WIKIPEDIA test sets in the probabilistic track.

/pdf/camb-at-cwi-shared-task-2018-complex-word-identification-br474kubuk.pdf

CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting.

/pdf/text-readability-assessment-for-second-language-learners-48gte61sn7.pdf

Ekaterina Kochmar

Papers

Grammatical error correction using hybrid systems and type filtering

Text Readability Assessment for Second Language Learners

Classification of Twitter Accounts into Automated Agents and Human Users

CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting.

Text Readability Assessment for Second Language Learners