FLLTP Sinclair & Renouf R mer Ljung Collins COBUILD 2005 BNC 12CWJZ20 10BYY008

Transcription

1 58 * 1. Sinclair & Renouf R mer Ljung Collins COBUILD Coniam BNC 2. * 12CWJZ20 10BYY008

2 59 3. frequency hypothesis noticing hypothesis 1 Hatch & Wagner - Gough 1976 Range15 Range 2000 Schmit % Base % % % Base15 Not in the < head > lists chain - locked clear - cut self - engrossed BNC COCA Base not in the base form lists 2 1. Range BNC % % Base % % Base % % Base % % Base % % Not in the lists % % Base % Not in the lists Range Range Not in the lists

3 60 Base % Base % Base % % 5 Range 5 #* Base 1-2 ** *** # Base % 8 Base1-2 #** ** # # involve to involve sb / sth to involve doing sth to be involved with involve self outer other self - involved # * * * # 487 Base 1-2 *** ant carpet liar ** Base % Not in the lists 7. 82% 3. #*** *** # #** 164 ** # 119 Base 1-2 # 73 Base 3-5 * 3 ** Base 6-8 ** Base 9-14 Not in the lists # 2 2. Range BNC Base 1-2 # Base 3-5 *** #*** Base 6-8 Excel ** #** Base 9-14 # Not in the lists # 4 Range Base

4 % % % % % % % % % 5 Pimsleur % Nation < % % % % % % % % % % % 3. 03% 1221 prior Prior #*** prior 2 prior 3 prior

5 62 BNC 1 In some universities prior to the beginning of prior 1 the first academic year college students will take an exam to identify their prior knowledge and learning 6 prior to difficulties in English 3 prior A person s prior experience will greatly prior prior 13 prior prior affect his behavior prior to prior 6. BNC 1 prior PRIOR 3581 % prior to % prior % prior % prior prior % prior prior priority a top / high priority for COCA the first priority is to on the priority list prior to prior adj. the first priority of our first priority is to to give 2 prior to priority to the start / beginning of World War I / II the study the interview the e- lection the end of Prior adj. prior research studies approval convictions year /years week month knowledge experi- ence learning prior 6. prior to prior adj. Range 2 Wordsmith Ex- prior cel 1 1 Antconc BNC prior to < w PRP > \ bprior \ b \ s \ bto \ b \ prior < w AJ0 > \bprior \b \s prior < w AV0 > \bprior \b \s prior < w NN1 > \bprior \b \s 2 COCA http / /corpus. byu. edu /coca / Search String Word s prior to * * * * prior to Word s prior. j* * prior

6 63 learner input The use of an EFL textbook corpus in corpus linguistics and language teaching. In Guy Aston Silvia Bernardini & Dominic Stewart Eds.. Corpora and Language Learners. Amsterdam John Benjamins. R mer Ute Progressives Patterns Pedagogy A Corpus - Driven Approach to English Progressive Forms Functions Contexts and Didactics. Amsterdam John Benjamins. Schmit R The role of consciousness in second language learning. Applied Linguistics 1. pp Sinclair J. & A. Renouf A lexical syllabus for language learning. In R. Carter & McCarthy eds.. Vo- cabulary and Language Teaching. London Longman Brown Coniam D A lexical view of the language needed 2 by primary students in HongKong An examination of word 2009 frequency in English language primary material. CUHK 2 Journal of Primary Education 5. pp Hatch E. & J. Wagner - Gough Explaining sequence and variation in second language acquisition. 1 Language Learning Special Issue 4. pp Ljung M A Study of TEFL Vocabulary. Stockholm 7 Almqvist and Wiksell International. Nation S. P Teaching and Learning Vocabulary. Beijing Foreign Language Teaching Research Press Pimsleur P A memory schedule. Modern Lan- 1 1 guage Journal 2. pp R mer Ute Looking at looking Functions and 9 contexts of progressives in spoken English and school English. In Antoinette Renouf & Andrew Kehoe Eds.. The Changing Face of Corpus Linguistics. Amsterdam Rodopi. R mer Ute Comparing real and ideal language

7 95 be improved. Key words computer-based English course book criteria checklist evaluation method A corpus-based study on four-word clusters in English for science and technology papers by YANG Yuanyuan Abstract To retrieve high-frequency four-word clusters in a self-made EST English for Science and Technology Paper Corpus corpus software Wordsmith Tools is used. Clusters searched out from the EST Paper Corpus are investigated from both structural and functional perspectives. Structural classification shows that clusters containing noun phrases with of in them clusters containing prepositional phrases and clusters including passive verbs are most widely used in the corpus. Functional classification of clusters reveals that referential clusters are most frequently used. In addition more than 40% of the clusters are related to professional topics in the field of natural sciences. In EST paper writing acquaintance with formulaic patterns of clusters in papers written by English native authors can help Chinese scientific and technical personnel develop an appropriate English writing style thus to facilitate international communication of technologies. Key words corpus EST papers four-word clusters structure function A corpus-based empirical study of quantitative evaluation of college English E-C translation quality by WANG Jianjia Abstract In this research the E-C translations from a self-built corpus of College English Competence Test were first manually annotated with translation errors according to potential evaluative parameters then the translation errors were concordanced in the light of each potential evaluative parameter and statistically analyzed for correlations with the E-C translations formal manual scorings respectively. Thus some statistically significant evaluative parameters that are actually functioning in E-C translation manual scorings were tentatively identified and verified by an automatically constructed evaluation model of multiple linear regression whose evaluation performances bear a Pearson correlation of with the formal manual scorings. So it can be concluded that the study tentatively reveals the translations text features that raters have been paying close attention to in current E-C translation scoring practice for college English testing from an empirical perspective and it might as well shed some light on automatic quantitative evaluation of E-C translations in language testing. Keywords E-C translation quantitative evaluation annotated corpora statistical analysis Using corpora in vocabulary teaching on a lexical grading basis by KONG Lei & QIN Hongwu Abstract This study explores the use of corpus tools in English vocabulary teaching. By employing the tools like Range Excel match calculation and frequency count we divided the textbook vocabulary into 15 levels and classified them into two types the productive and the receptive. In line with the frequency of the productive and receptive vocabularies we managed to work out a revised list of words for classroom teaching. Moreover we find that the distribution of the vocabulary in the exercises should be adequately analyzed so that we can clarify the interrelations between the exercises and the texts and adequately evaluate the role that exercises play in vocabulary acquisition. Key words vocabulary difficulty productive vocabulary receptive vocabulary frequency counts