北科大經(jīng)管課件02-經(jīng)濟學概述(2)

1、1 A Corpus-based Study of the Use of MAKE in Chinese Student Writing 蘇正造蘇正造 (.tw) 國立臺灣戲曲學院 國立臺灣師範大學博士班學生 2 Outline I. Introduction II. Literature III. Methodology and Corpus IV. Overall Frequency of MAKE V. Uses of MAKE VI. Conclusion 3 I. Introduction Chinese learners use of MAKE: This pa

2、per focus on the use of MAKE in the writing of university students in China and in Taiwan by the Chinese Learner English Corpus (CLEC) and the Soochow Colber Student Corpus (SCSC). Benchmark: BNC vs. LOCNESS Previous studies: overuse of MAKE by Taiwanese and Swedish students, but underuse by French

3、students (Benchmark: LOCNESS (Louvain Corpus of Native English Essays). This study: all groups of students overuse a wide range of MAKE (Benhmark: BNC) The results show that LOCNESS represents only native students writing but not the norm of academic writing, and may not be a suitable benchmark as a

4、 native reference model. 4 II. Literature Review What is Corpus? A collection of naturally occurring language text chosen to characterize a state or variety of a language. John Sinclair (1991) A collection of naturally occurring examples of language, consisting of anything from a few sentences to a

5、set of written texts or tape recordings, which have been collected for linguistic study. Susan Hunston (2002) Learner Corpus: systematic computerized collections of texts produced by learners. Nadja Nesselhauf (2004) 5 English corpus BROWN Corpus 500 texts of 2000 words each (1,000,000 words) BNC: 1

6、00 m words carefully compiled (10 m words spoken data) American National Corpus (ANC) / When completed, the ANC will contain a core corpus of at least 100 million words, comparable across genres to the British National Corpus (BNC). ICE: International Corpus of Englis

7、h http:/www.ucl.ac.uk/english-usage/ice/ 500 texts of approximately 2,000 words (speech and writing votpud) Hong Kong, East Africa, Great Britain, India, New Zealand, Philippines, Singapore Linguistic Data Consortium / The Linguistic Data Consortium supports language-related e

8、ducation, research and technology development by creating and sharing linguistic resources: data, tools and standards. 6 Learner corpus ICLE: International Corpus of Learner English http:/cecl.fltr.ucl.ac.be/Cecl-Projects/Icle/icle.htm Essays on different topic by advanced learners of English as a f

9、oreign language 2 million words of EFL writing from learners representing 11 different mother tongue backgrounds (Bulgarian, Czech, Dutch, Finnish, French, German, Italian, Polish, Russian, Spanish, Swedish). 181.50 for Individual license (http:/ Chinese learner of English Soochow Colber Student Cor

10、pus Chinese Learner English Corpus (CLEC) 7 Corpus and language teaching Native speaker corpus Non-native speaker corpus Teaching data-driven learning Pedagogic material Nadja Nesselhauf (2004) 8 Studies on MAKE Altenberg & Granger (2001): Corpus: Learner corpus: International Corpus of Learner Engl

11、ish (ICLE) (advance learners) FR: French speaking learner of English SW: Swedish speaking learner of English Reference Corpus: Louvain Corpus of Native English Essays (LOCNESS) Native speaker American students Results 9 Yang, Hsiao-Hui (2005) Corpus: Learner Corpus: Taiwanese Vocatinal College Stude

12、nt Corpus (TVCSC) Students written assignments in a vocational college in Taiwan with a variety of topics. Reference Corpus: Louvain Corpus of Native English Essays (LOCNESS) Native speaker American students Results 10 III. Methodology and Corpus Contrastive Analysis 11 Corpus used in previous studi

13、es International Corpus of Learner English (ICLE) FR: essays written by advanced French-speaking learners of English with 169,190 words. SW: essays written by advanced Swedish-speaking learners of English with 169,608 words. Louvain Corpus of Native English Essay (LOCNESS) argumentative essays writt

14、en by native-speaker American students British pupils A level essays: 60,209 words British university students essays: 95,695 words American university students essays: 168,400 words Total number of words: 324,304 words (168,325 words) Taiwanese Vocational College Student Corpus (TVCSC) TVCSC: stude

15、nts writing assignments in a vocational college in Taiwan (various topics) with 136,552 words. 12 Corpus used in this study Chinese Learner English Corpus (CLEC) Essays written by 6 different levels of English learners in colleges in China Soochow Colber Student Corpus (SCSC) Essays written by Engli

16、sh majors and non English majors in NTU and Soochow University in Taiwan BNC: British National Corpus 100 m words carefully compiled 10 m words spoken data Academic written sub-corpus: humanities, arts, medicine, natural science, politics, law, education, social science, technology, engineer 13 BNC

17、http:/www.natcorp.ox.ac.uk/ 14 Tools BNC SARA VIEW: Variation In English Words and Phrases (Mark Davies) / AntConc (Laurence Anthony): http:/www.antlab.sci.waseda.ac.jp/ 15 SARA: corpus list 16 SARA: search result 17 VIEW: VARIATION IN ENGLISH WORDS AND PHRASES /

18、18 Search BNC in VIEW 19 AntConc http:/www.antlab.sci.waseda.ac.jp/ 20 Research questions Are advanced English learners over- or under-use of MAKE? Do Chinese learners present the same trend of overuse and underuse as other learners? Is LOCNESS (native American students argumentative essays) a suita

19、ble corpus to be a benchmark of overuse and underuse of MAKE? What factors influence the use of MAKE? Learners proficiency: Does students proficiency influence their use of MAKE? Topic/ Genre/ Register: Is MAKE a style-sensitive verb. Does the Frequency of MAKE appear differently in different genres

20、 of corpus. Have advanced learner master the use of MAKE? 21 IV. Overall Frequency of MAKE MAKE in Different Corpus 22 MAKE in BNC 23 MAKE in Academic of BNC 24 MAKE in Chinese Learner English Corpus (CLEC) ST2: 高中生 ST3:大學12年級(非英文系) ST4:大學34年級(非英文系) ST5:大學12年級(英文系) ST6:大學34年級(英文系) 25 MAKE in Soochow

21、 Colber Student Corpus (SCSC) 26 Summary All English learners tend to overuse MAKE in their writing. MAKE is a style-sensitive verb. The Frequency of MAKE appears differently in different genres of corpus. Proficiency vs. Use of MAKE: The more proficient a learner is, the less overuse of MAKE. LOCNE

22、SS may not be a suitable corpus as a benchmark for native English speakers academic writing. 27 V. Uses of MAKE Major uses of the verb MAKE 1. Delexical uses: Carrying out an action make a distinction/a decision/a reform 2. Causative uses: Causing or changing make sb believe sth, make sth possible 3

23、. Creating or producing make furniture, make a hole, make a law, make a fortune, make a living 4. Link verb usesShe will make a good teacher. 5. Others (stating an amount or time, phrasal verbs) if we run, we should make it, make out, make up, make out of, make good 28 Uses of MAKE in different lear

24、ner corpus Vs. Uses of MAKES in BNC academic corpus 29 Uses of MAKE in different learner corpus Vs. Uses of MAKES in BNC academic corpus 1. Deliexical and causative uses have the high frequency (more than 70%) 2. The lowest frequency of use is “Link verb uses”. 3. The most overuse of MAKE is “Creati

25、ng or producing”. 4. BNC has 15% of Others category, which means a need of further categorization. 30 Causative uses Causative MAKE is a complex-transitive verb with three types of object + complement structure. Adjective: make something possible. Verb: make somebody understand something Noun: make

26、somebody a star 31 Causative uses of MAKE 1. All learners underuse Adjectives and Nouns, and overuse Verb while Chinese learners show especially much more underuse and overuse. 2. LOCNESS SW/FR SCSC/CLEC-ST6 TVCSC 32 Causative MAKE with verb complements: semantic types of verb The verbs are subdivid

27、ed into three semantic categories. Relational: seem, appear, become, feel, look, get Mental: think, know, understand, realize, believe, forget, remember, concentrate, perceive Actional: work, pay, change, happen, suffer, decide 33 Causative MAKE with verb complements: semantic types of verb 1. Ratio

28、nal: SW, FR and LOCNESS tend to underuse Rational type while Chinese learners tend to overuse. 2. Mental: All learners tend to overuse Mental type. 3. Actional: LOCNESS underuse Actional type while TVCSC and CLEC overuse it. Chinese learners rely more on actional verb. 34 Conclusion All English learners tend to overuse MAKE in their writing. Chinese learners show some different trend of overuse and underuse in the causative