医学英语常用专词汇－王京

hension. The dramatically large number of English words, however, is a learning goal far eserved. q The article is co-authored equally. * Corresponding author. Tel.: +86 29 8477 4475; fax: +86 29 8323 4516. E-mail address: guangcge@fmmu.edu.cn (G.-c. Ge). Available online at www.sciencedirect.com English for Specific Purposes 27 (2008) 442–458 www.elsevier.com/locate/esp ENGLISH FOR SPECIFIC PURPOSES 0889-4906/$34.00 � 2008 The American University. Published by Elsevier Ltd. All rights r doi:10.1016/j.esp.2008.05.003 1. Introduction The acquisition of vocabulary has long been considered to be a crucial component of learning a language (Coady, Magoto, Hubbard, Graney, & Mokhtari, 1993; Nation, 2001) because the breadth and depth of a student’s vocabulary will have a direct influence upon the descriptiveness, accuracy and quality of his or her writing (Read, 1998). Nagy (1988) also claimed that vocabulary is a major prerequisite and causative factor in compre- Abstract This paper reports a corpus-based lexical study of the most frequently used medical academic vocabulary in medical research articles (RAs). A Medical Academic Word List (MAWL), a word list of the most frequently used medical academic words in medical RAs, was compiled from a cor- pus containing 1093011 running words of medical RAs from online resources. The established MAWL contains 623 word families, which accounts for 12.24% of the tokens in the medical RAs under study. The high word frequency and the wide text coverage of medical academic vocabulary throughout medical RAs confirm that medical academic vocabulary plays an important role in med- ical RAs. The MAWL established in this study may serve as a guide for instructors in curriculum preparation, especially in designing course-books of medical academic vocabulary, and for medical English learners in setting their vocabulary learning goals of reasonable size during a particular phase of English language learning. � 2008 The American University. Published by Elsevier Ltd. All rights reserved. Establishment of a Medical Academic Word Listq Jing Wang, Shao-lan Liang, Guang-chun Ge * Department of Foreign Languages, Fourth Military Medical University, Xi’an, China Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 nedy (1994) reported that academic vocabulary accounted for 8.4% of the tokens in the Learned and Scientific sections of the LOB and Wellington corpora, and for 8.7% of J. Wang et al. / English for Specific Purposes 27 (2008) 442–458 443 the tokens in economics texts. Coxhead (2000) reported that the academic vocabulary in her Academic Word List covered 10% of the tokens in her 3500000 running word aca- demic corpus. Santos’ research (2000) revealed that roughly 16% of the words in his text- book samples across different disciplines were academic words. This high coverage of academic words in the academic texts has far exceeded the 5% ratio of the unknown to the known comprehension threshold suggested by Laufer (1988), who has pointed out that a learner has to know 95% of the words in a text to ensure reasonable comprehension of the text because the ratio of unknown to known words over 5% is not sufficient to allow reasonably successful guessing of the meaning of the unknown words. In addition, Kuehn (1996) observed that knowledge of academic words differentiated academically well-pre- pared from under-prepared college students from all backgrounds. The findings from these studies clearly indicate that EAP learners, without sufficient knowledge of academic vocabulary, cannot deal effectively with reading materials for various types of academic tasks they are supposed to fulfill (Laufer & Nation, 1999). However, proficient use of aca- demic vocabulary is one of the most challenging tasks in ESP students’ word expansion. Anderson and Freebody (1981) found that academic words were the words most often identified as unknown by her students in academic texts. Based on his study, Farrell beyond the reaches of second language learners and even beyond the reaches of most native speakers. Fortunately, all words are not equally important in different stages of learning. Nation’s (2001) division of vocabulary into four levels — high frequency words, academic vocabu- lary, technical vocabulary and low frequency words — indicates that some words deserve more attention and effort than others in different phases of language learning or for differ- ent purposes. According to Nation and Waring (1997), it is generally agreed that the beginners of English learning should focus on the first 2000 most frequently occurring word families of English in the General Service List (GSL) (West, 1953), while for inter- mediate or advanced learners who usually study English for academic purposes, the com- mand of these GSL words may no longer be their major concern and the priority of their vocabulary acquisition may be shifted to lower frequency vocabulary. In academic set- tings, ESP students do not see these technical terms as a problem because these terms are usually the focus of the discussion in the classroom or are glossed in the textbook (Strevens, 1973). The vocabulary that ESP students have most difficulty with is known, in ESP jargon, as non-subject-specific semi-technical vocabulary or academic vocabulary (Li & Pemberton, 1994; Shaw, 1991; Thurstun & Candlin, 1998). 1.1. Academic vocabulary Academic vocabulary, which is also called sub-technical vocabulary (Cowan, 1974) or semi-technical vocabulary (Farrell, 1990), is viewed as ‘‘formal, context-independent words with a high frequency and/or wide range of occurrence across scientific disciplines, not usually found in basic general English courses; words with high frequency across sci- entific disciplines” (Farrell, 1990, p. 11). The high frequency occurrence of academic words in academic text has been confirmed by some researchers. Sutarsyah, Nation, and Ken- (1990) reported that the lack of knowledge was partly the result of the assumption of some Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 selected from engineering textbooks in 13 engineering disciplines and produced an aca- demic word list of 1200 word families for engineering students. The word families in 444 J. Wang et al. / English for Specific Purposes 27 (2008) 442–458 her word list are frequently encountered in engineering textbooks compulsory for all engi- neering students, regardless of their fields of specialization. She argued that academic subject teachers that their students knew these words and as a result they seldom taught these words explicitly. 1.2. Previous studies on academic vocabulary list development Previous studies on academic vocabulary have produced some very helpful academic word lists. Quite a number of these academic word lists focused on the academic vocabulary occurring across different disciplines. By analyzing 301800 words in textbooks and lectures published in journals covering 19 academic disciplines, Campion and Elley (1971) devel- oped a word list containing 500 most common words and 3200 frequently used words. The items in their list represented the vocabulary that students were likely to encounter in their university studies. Praninskas (1972) compiled the American University Word list, which was based on a corpus of 272466 words from 10 university-level textbooks covering 10 academic disciplines. Lynn’s (1973) and Ghadessy’s (1979) word lists were drawn up by counting the words for which foreign students wrote annotations in their university text- books and the words that the students had found difficult during their reading. Xue and Nation (1984) combined the four earlier-compiled word lists (Campion and Elley’s, Pra- ninskas’s, Lynn’s, and Ghadessy’s) into the University Word List (UWL), consisting of about 800 words that were not in the first 2000 words of the GSL but that were of high fre- quency and of wide range in academic texts. Xue and Nation’s purpose of setting up the UWL was to create a list of high frequency words for learners with academic purposes, so that these words can be taught and directly studied in the same way as the words from the GSL. More recently, Coxhead (2000) developed the Academic Word List (AWL), using a corpus of 3.5 million running words, plus Range—the software which could calculate how often a word occurred (its frequency) and in how many different texts in the corpus it occurred (its range). The texts in her corpus were selected from different academic journals and university textbooks in four main areas: arts, commerce, law and natural science. The AWL contains 570 word families that account for approximately 10% of the total words in her selected academic texts. Compared with the UWL, the AWL contains fewer word fam- ilies but provides more text coverage andmore consistent word selection criteria. AWL now is a widely cited academic word list across a broad range of disciplines. In addition to these discipline-crossing academic word lists, some researchers have focused on the academic vocabulary used in a single discipline. They assumed that there might be some unique features in the academic vocabulary across sub-disciplines of one discipline. Lam (2001) conducted an empirical study of academic vocabulary of Computer Science in order to find the vocabulary problems encountered by the computer science stu- dents in reading academic texts. She noted that academic vocabulary was semantically dis- tinct from the same vocabulary when it appeared in general texts. She suggested that such lexical terms should be presented as a glossary of academic vocabulary with information of frequency of occurrences based on a specialized corpus. Mudraya (2006) established the Student Engineering English Corpus (SEEC), containing nearly 2000000 running words vocabulary should be given more attention in the ESP classroom. Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Despite the academic vocabulary lists across different disciplines compiled respectively by some researchers, there were few detailed studies exclusively on medical academic vocabulary used in the field of medicine. Baker (1988) analyzed three rhetorical items in medical journal articles and she concluded that rhetorical items were in the category of academic vocabulary and that identifying academic items had some pedagogical implica- tions. Chen and Ge (2007) analyzed the occurrence and distribution of the AWL word language learning. ers/users of English for Medical Purposes (EMP). J. Wang et al. / English for Specific Purposes 27 (2008) 442–458 445 2.1.1. Data collection All the written medical RAs to be adopted in the corpus were downloaded from the database ScienceDirect Online (http://www.sciencedirect.com), the world’s largest elec- tronic collection of science, technology and medicine with full text and bibliographic infor- mation, accessed at the library of the Fourth Military Medical University (FMMU). The database ScienceDirect Online contains over 1800 journals, including almost every top title across 24 disciplines from natural science to social science, and is considered to be one of the most authoritative and representative databases. In the discipline of Medicine and Dentistry of ScienceDirect Online, there were 32 sub- ject areas at the time of our study, covering almost all the fields of medical science. The samples in the corpus were chosen from the following 32 subject areas. 1. Anesthesiology and Pain Medicine 17. Medicine and Dentistry 2. Cardiology and Cardiovascular Medicine 18. Nephrology 3. Clinical Neurology 19. Obstetrics, Gynecology and Women’s Health 2. Methodology 2.1. Corpus establishment We established as the database for our study a written specialized corpus containing 1093011 running words from 288 written texts of a single genre—medical research arti- cles, because reading and writing medical RAs is the fundamental concern for most learn- families in medical RAs. Their findings confirmed that the academic vocabulary had a high text coverage and dispersion throughout a medical research article and served some important rhetorical functions, but they argued that the AWL was far from complete in representing the frequently used medical academic vocabulary in medical RAs and called for efforts in establishing a medical academic word list. The study reported in this paper was designed to develop a Medical Academic Word List (MAWL) of the most frequently used medical academic vocabulary across different sub-disciplines in medical science. We hope the MAWL established in this study may serve as a guide for medical English instructors in curriculum preparation, especially in design- ing course-books of medical academic vocabulary, and for medical English learners in set- ting their vocabulary learning goals of reasonable size during a particular phase of English Line missing Administrator 文本下划线工具 446 J. Wang et al. / English for Specific Purposes 27 (2008) 442–458 All the sample medical RAs included in the corpus were kept at their original length, written in the internationally conventionalized IMRD (Introduction–Method–Result–Dis- cussion) structure, published in the years 2000–2006 and written by native English speak- ing writers by Wood’s (2001) ‘‘strict” criteria (first authors had to have names native to the 4. Complementary and Alternative Medicine 20. Oncology 5. Critical Care and Intensive Care Medicine 21. Ophthalmology 6. Dentistry, Oral Surgery and Medicine 22. Orthopedics, Sports Medicine and Rehabilitation 7. Dermatology 23. Otorhinolaryngology and Facial Plastic Surgery 8. Emergency Medicine 24. Pathology and Medical Technology 9. Endocrinology, Diabetes and Metabolism 25. Perinatology, Pediatrics and Child Health 10. Forensic Medicine 26. Psychiatry and Mental Health 11. Gastroenterology 27. Public Health and Health Policy 12. Health Informatics 28. Pulmonary and Respiratory Medicine 13. Hematology 29. Radiology and Imaging 14. Hepatology 30. Surgery 15. Immunology, Allergology and Rheumatology 31. Transplantation 16. Infectious Diseases 32. Urology country concerned and also be affiliated with an institution in countries where this lan- guage is spoken as the first language). A three-round selection was conducted in choosing the sample medical RAs for the cor- pus. In the first round, we took each of the 32 subject areas as one stratum and then by stratified random sampling we selected 3 journals from each of the 32 subject areas/stra- tum, totaling 96 journals. In the second round, we randomly selected one issue out of each of the 96 journals obtained in the first round. From the 96 selected issues, the articles which were not following the IMRD format, were not written by native English speaking writers or were shorter than 2000 running words or longer than 12000, running words were eliminated. In the third round, we selected 3 criteria-fulfilling articles from each of the 96 issues by simple random sampling. After this three-round selection, 288 texts were chosen for the corpus, the shortest one containing 2923 running words and the longest one containing 10901 running words (4939 on average). 2.1.2. Data processing In this study, data processing incorporated the standardization of the medical RAs to be stored in the corpus and the normalization of the words in the to-be-stored RAs. For the standardization of the medical RAs included in the corpus, the charts, diagrams, bib- liographies and some components in texts, which were not able to be processed by com- puter analyzing programs or should not be included in the lexical analysis in the chosen medical RAs, were removed so as to eliminate the factors unrelated to the lexical analysis and to ensure that the texts stored in the corpus be readable by the computer software. The Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 J. Wang et al. / English for Specific Purposes 27 (2008) 442–458 447 normalization of words was fulfilled automatically by the computer software. The com- puter software would read all inflections or derivations of a word as its basic form and would count the range and frequency of them as one word family. For example, induce, induced, induces, inducing and induction would be counted as one word by the computer software. Word family, as defined by Bauer and Nation (1993), is the base word plus its inflected forms and transparent derivations, including all closely related affixed forms as well as the stem’s most frequent, productive and regular prefixes, suffixes and perceived transparency. According to Coxhead (2000, p. 218), ‘‘comprehending regularly inflected or derived members of a family does not require much more effort by learners if they know the base word and if they have control of basic word-building processes”, which may account for the general adoption of the word family in many word lists. After the stan- dardization of the sample texts and normalization of words, the words in the corpus were counted and sorted automatically by computer. 2.2. List development 2.2.1. Word selection criteria The three principles (specialized occurrence, range and frequency of a word family) used by Coxhead in developing the AWL were adopted in our study with some adjust- ment. In her study, Coxhead named wide-range word families as the word families whose members occur in at least half of the 28 subject areas in her corpus. In this study, we also set 50% as the criterion for inclusion. The members of a word family to be included in the MAWL should occur in 16 subject areas, half of the 32 subject areas in our corpus. The least frequency of the members of a word family to be included in the MAWL was 30 times, a third of Coxhead’s 100 times, for the number of the running words (1000000) in our corpus was only about one third of that (3500000) in Coxhead’s corpus. Coxhead (2000) also reported that in her AWL word selection, range was the first cri- terion and frequency the second because a word count based mainly on the frequency would have been biased by longer texts and topic-related words. This principle was also applied in the present study. Only word families covering 16 subject areas or more would be included in the MAWL, while word families occurring with very high frequency but covering fewer than 16 subject areas would be excluded. In sum, all the finally included word families in the MAWL met the following word selection criteria: 1. Specialized occurrence: The word families included had to be outside the first 2000 most frequently occurring words of English, as represented by West’s GSL (1953). 2. Range: Members of a word family had to occur at least in 16 or more of the 32 subject areas. 3. Frequency: Members of a word family had to occur at least 30 times in the corpus of medical research articles. As is known, the division between technical vocabulary and academic/sub-technical vocabulary is not always distinct (Chung & Nation, 2003; Mudraya, 2006). In some cases, arbitrary decisions need to be made to distinguish technical vocabulary and academic/sub- technical vocabulary. In compiling the MAWL, two experienced professors of English for Medical Purposes from our department were consulted whenever any arbitrary decision Administrator 文本下划线工具 Administrator 文本下划线工具 Administrator 文本下划线工具 was needed in the inclusion or the elimination of some criteria-fulfilling controversial word families in or from the computer-screene