A Corpus-based Comparison between the Academic Word List and the Academic Vocabulary List

This study was a corpus-based comparison between two lists of academic words: Coxhead’s (2000) Academic Word List (AWL) and Gardner and Davies’ (2014) Academic Vocabulary List (AVL). Comparisons were made between different types of lexical coverage provided by the AWL and the AVL in the self-created University Academic Corpus (72-million tokens). The findings indicated that the performance of the AWL and the AVL was different when different evaluation criteria were adopted. Implications, limitations, and suggestions are listed for future research

Creating a Korean Engineering Academic Vocabulary List (KEAVL): Computational Approach

With a growing number of international students in South Korea, the need for developing materials to study Korean for academic purposes is becoming increasingly pressing. According to statistics, engineering colleges in Korea attract the largest number of international students (Korean National Institute for International Education, 2018). However, despite the availability of technical vocabulary lists for some engineering sub-fields, a list of vocabulary common for the majority of the engineering sub-fields has not yet been built. Therefore, this study was aimed at creating a list of Korean academic vocabulary of engineering for non-native Korean speakers that may help future or first-year engineering students and engineers working in Korea. In order to compile this list, a corpus of Korean textbooks and research articles of 12 major engineering sub-fields, named as the Corpus of Korean Engineering Academic Texts (CKEAT), was compiled. Then, in order to analyze the corpus and compile the preliminary list, I designed a Python-based tool called KWordList. The KWordList lemmatizes all words in the corpus while excluding general Korean vocabulary included in the Korean Learner’s List (Jo, 2003). Then, for the remaining words, KWordList calculates the range, frequency, and dispersion (in this study deviation of proportions or DP (Gries, 2008)) and excludes words that do not pass the study’s criteria (range ≥ 6, frequency ≥ 100, DP ≤ 0.5). The final version of the list, called Korean Engineering Academic Vocabulary List or KEAVL, includes 830 lemmas (318 of intermediate level and 512 of advanced level). For each word, the collocations that occur more than 30 times in the corpus are provided. The comparison of the coverage of the Korean Academic Vocabulary List (Shin, 2004) and KEAVL based on the Corpus of Korean Engineering Academic Texts showed that KEAVL covers more lemmas in the corpus. Moreover, only 313 lemmas from the Korean Academic Vocabulary List (Shin, 2004) passed the criteria of the study. Therefore, KEAVL may be more efficient for engineering students’ vocabulary training than the Korean Academic Vocabulary List and may be used for the engineering Korean teaching materials and curriculum development. Moreover, the KWordList program written for the study can be used by other researchers, teachers, and even students and is open access (https://github.com/HelgaKr/KWordList)

Introducing a Romanian Frequency List and the Romanian Vocabulary Levels Test

Vocabulary is considered essential to language learning, thus English word lists and tests based on frequency information have become the centre of attention for researchers, teachers and learners alike. As a result, it is argued hereby that frequency based word lists and tests should be adapted and regarded as key elements for teaching and learning Romanian as an additional language as well. Since there are currently no reliable frequency lists and lexical tests in Romanian, this paper aims to bridge this gap by introducing the first Romanian Word List and the Romanian Vocabulary Levels Test. The list contains the 10,000 most frequent Romanian words and is based on the Romanian Balanced Annotated Corpus (ROMBAC, Ion, Irimia, Ștefănescu, Tufiș 2012). The primary objective of the paper is to elaborate on the compilation criteria, the challenges involved and the benefits of such a list in the case of teaching, learning and curriculum design for Romanian as an additional language. The secondary objective is to present a practical application of the word list by introducing an exemplary Romanian lexical test, the Romanian Vocabulary Levels Test and examine its reliability and validity

Effects of corpus-based instruction on phraseology in learner English

This study analyses the effects of data-driven learning (DDL) on the phraseology used by 223 English students at an Italian university. The students studied the genre of opinion survey reports through paper-based and hands-on exploration of a reference corpus. They then wrote their own report and a learner corpus of these texts was compiled. A contrastive interlanguage analysis approach (Granger, 2002) was adopted to compare the phraseology of key items in the learner corpus with that found in the reference corpus. Comparison is also made with a learner corpus of reports produced by a previous cohort of students who had not used the reference corpus. Students who had done DDL tasks used a wider range of genre-appropriate phraseology and produced a lower number of stock phrases than those who had not. The study also finds evidence that students use more phrases encountered in paper-based concordancing tasks than in hands-on tasks.Unlike in previous DDL studies, observations of the learning of a specific text-type through DDL in the present study are based on the comparison with both a control learner corpus and an expert corpus.The study also considers the use of DDL with a large class size

Analysing lexical richness in French learner language: what frequency lists and teacher judgements can tell us about basic and advanced words

We show how teacher judgements can be used to assess the quality of vocabulary used by L2 learners of French

A corpus-based lexical analysis of Chinese medicine research articles.

This is the final version of the article. Available from the publisher via the URL in this record.This study investigates the usefulness of two academic word lists - Coxhead’s (2000) Academic Word List (AWL) and Gardner & Davies’ (2014) Academic Vocabulary List (AVL) - for students of English for Chinese Medical Purposes. The two academic word lists were evaluated in terms of the coverage they achieved in a corpus of Chinese medical research articles (CMRAs) written in English. The AWL was found to cover 10.64% of tokens in the corpus, while the AVL was found to cover 21.17% overall. In both cases, the majority of the coverage was achieved by a relatively small subset of the lexical items on the lists. Analysis of the most frequently used words that are not included in the General Service List, Academic Word List and Academic Vocabulary List in the CMRAs shows that a small number of such words achieve a high level of coverage, suggesting that they should be given a great deal of attention by learners in this discipline. This suggests that a discipline-specific listing would be of great benefit to learners in this discipline. A list of the most prominent 100 off-list lexical items is provided

A COMPARISON OF THE ACADEMIC WORD LIST AND THE ACADEMIC VOCABULARY LIST: SHOULD THE AVL REPLACE THE AWL?

In this commentary, we begin with the discussion on a brief history of academic wordlists. Adopting a comparative perspective, then, the merits and demerits of the Academic Word List (AWL) (Coxhead, 2000) and its competing counterpart the Academic Vocabulary List (AVL) (Gardner & Davies, 2014) are presented. We also explore whether the AWL can still be considered as “the best list†(Nation, 2001, p. 12) for improving academic words, or whether its counterpart is reasonably “the most current, accurate, and comprehensive list†(Gardner & Davies, 2014, p. 325). The comparison was made in terms of twelve aspects: corpus size, types of corpus texts, sources of corpus texts, text balance, disciplines included, counting unit, wordlist items, method for excluding highfrequency words, minimum frequency, method for excluding technical words, sequence of list items and lexical coverage. The comparison reveals that the AVL is far from complete and cannot replace the AWL. The results of the comparison can have implications for practitioners and course developers

Termhood-based Comparability Metrics of Comparable Corpus in Special Domain

Cross-Language Information Retrieval (CLIR) and machine translation (MT) resources, such as dictionaries and parallel corpora, are scarce and hard to come by for special domains. Besides, these resources are just limited to a few languages, such as English, French, and Spanish and so on. So, obtaining comparable corpora automatically for such domains could be an answer to this problem effectively. Comparable corpora, that the subcorpora are not translations of each other, can be easily obtained from web. Therefore, building and using comparable corpora is often a more feasible option in multilingual information processing. Comparability metrics is one of key issues in the field of building and using comparable corpus. Currently, there is no widely accepted definition or metrics method of corpus comparability. In fact, Different definitions or metrics methods of comparability might be given to suit various tasks about natural language processing. A new comparability, namely, termhood-based metrics, oriented to the task of bilingual terminology extraction, is proposed in this paper. In this method, words are ranked by termhood not frequency, and then the cosine similarities, calculated based on the ranking lists of word termhood, is used as comparability. Experiments results show that termhood-based metrics performs better than traditional frequency-based metrics

The nature of vocabulary in academic speech of hard and soft-sciences

Little is known about the similarities and differences between the vocabulary in hard-sciences (e.g., Maths, Engineering, Medicine) and soft-sciences (e.g., Business, Law, History), especially in spoken discourse. To address this gap, a Soft Science Spoken Word List (SSWL) was developed for second language learners of soft-sciences at English-medium universities. The list consists of the 1,964 most frequent and wide-ranging word-families in a 6.5 million word corpus of soft-science speech, which represents 12 subjects across two equally-sized sub-corpora. The list may allow learners to recognize 94%–97% of the words in academic speech of soft-sciences. A comparison of the SSWL with Dang's (2018) Hard Science Spoken Word List revealed that although the most frequent 3,000 words are important for comprehending academic speech of both soft- and hard-sciences, the value of these words in soft-sciences is greater than in hard-sciences. Pedagogical implications related to this nature of vocabulary in hard- and soft-science speech are provided