Lexical Coverage Research Articles

Vocabulary Demands of Academic Spoken English Revisited: A Case of University Lectures and TED Presentations

The article shines light upon the differences in the vocabulary demands of academic spoken discourse between three broad scientific disciplines: Life Sciences, Physical Sciences, and Social Sciences. By employing the Academic Word List (AWL) and British National Corpus/Corpus of Contemporary American English (BNC/COCA) wordlist, the present study analyzed data of the transcripts from 160 university lectures, 39 seminars, and 600 TED talks. Results from the analysis of the 2.5-million-token corpus demonstrated an order of lexical difficulty in which Life Sciences and Social Sciences were the most and least lexically demanding fields of study, correspondingly. Research findings also indicated a strong supportive relationship between the AWL and the BNC/COCA wordlist. Learners with limited vocabulary knowledge at 1,000 and 2,000 levels could significantly increase their lexical coverage for academic lectures, seminars, and presentations with the support of the AWL. For disciplines with low lexical demands like Social Sciences, the vocabulary knowledge of nearly 2,570 word families (BNC/COCA 2,000 + AWL) could help students understand 95.07% of the running words in universities lectures and seminars. Research findings offer implications for vocabulary teaching and learning.

Vocabulary in digital science resources for middle school learners

This corpus-based study examined the vocabulary in a 2.7 million token corpus composed of digital science resources for middle school (6–8 grade) students in the United States. The findings of this study showed that to reach the suggested 95%–98% lexical coverage thresholds of the Digital Science Corpus (DSC) that are conventionally deemed to facilitate minimal and optimal reading comprehension (Laufer, 2020), middle school (MS) students grade 6–8 must recognize the first 6,000, respectively 14,000 most frequent BNC/COCA (Nation, 2012) word families plus proper nouns and marginal nouns. The results of the lexical analysis across the three sub-corpora in the DSC suggest that the Life Science sub-corpora has a considerably larger vocabulary load than the Physical Science and Earth and Space Science sub-corpora. Additionally, while 98.60% of the most frequent 1,000 BNC/COCA word families occurred at least six times in the DSC, the 2,000–7,000 BNC/COCA word families provided significantly fewer opportunities for repeated occurrence. Since more than half of the words in the 5,000–7,000 BNC/COCA bands occurred five times or less in the overall corpus, most words across these bands do not have high enough frequency in the digital science resources to allow MS students to learn them incidentally from reading the texts found in digital science resources. Several pedagogically relevant suggestions are discussed.

Relationships between lexical coverage, learner knowledge, and teacher perceptions of the usefulness of high‐frequency words

The ChallengeForeign language learners should learn the words that are most useful for them. Information from various sources (corpora, teachers, and learners) has been used to identify these words. One question that arises is to what extent these data sets correlate to one another. This study aims to address this question.

A specialized vocabulary list from an original corpus of digital science resources for middle school learners

• A digital science corpus from middle school resources was assembled. • The corpus consists of approximately 2.7 million tokens. • A type-based specialized list was extracted using three instruments and seven inclusion criteria. • The lexical coverage of the lists was compared with the lexical coverage of four other secondary science lists. • The list could be used as a tool for specialized vocabulary learning in science classrooms where digital science resources are used.

How much vocabulary is needed for comprehension of video lectures in MOOCs: A corpus-based study.

Over the past years, Massive Open Online Courses (MOOCs) have emerged as new competitive advantages in the digital economy of higher education globally. Accordingly, an increasing number of individuals are attracted to these new learning environments for developing their knowledge and skills in a variety of subject areas. Despite these developments, research on linguistic features of MOOCs lectures as the main mediums for delivering the course contents remained limited. To address this gap, the present study analyzed a corpus of MOOCs lectures with around 4.45 million words to determine the size of vocabulary knowledge needed for 95 and 98% coverages. The findings revealed that sufficient coverage of the course contents requires knowledge of the 5,000 most frequent words in English. Nonetheless, achieving adequate coverage level requires a much larger vocabulary size of around 9,000 most frequent words in English. The study also found that widely used word lists for general and academic vocabulary (i.e., the GSL/AWL) fail to support MOOCs learners with sufficient vocabulary knowledge for adequate lexical coverage. Based on these findings, the study draws a number of implications for preparing non-native English speakers to use MOOCs effectively and setting research-informed vocabulary learning goals in instructional programs and materials.

Incidental vocabulary acquisition from listening to English teacher education lectures: A case study from Macau higher education.

Some proponents of higher education English as a medium of instruction (EMI) have suggested listening to English lectures provides students the opportunity to incidentally acquire unknown words. A case study was designed to examine this assumption. First, the lexical profiles of 27 Introduction to English Language Teaching first-year undergraduate course lectures were computed to determine how many words students need to know for comprehension. Then an incoming year-1 undergraduate student with an English vocabulary size of 7,500 word families and mastery of the most frequent 3,000 word families listened to these lectures across 13.5 weeks with the purpose of measuring incidental acquisition gains of three aspects of word knowledge for ten targeted words. Lastly, the student’s perceptions about listening to EMI lectures and potentials for this listening inducing incidental acquisition of word knowledge were gathered through a semi-structured interview. The lexical profiling of the entire corpus showed students need knowledge of the most frequent 4,000 English word families plus proper nouns and marginal words for 98% lexical coverage; however, some lectures present students with a more substantial lexical burden than the lectures overall. The student made the most gains in receptive meaning, followed by receptive form, and finally productive meaning. Content analysis of the interview transcript found seven themes representing the student’s perception about listening to EMI lectures and their potential for inducing incidental vocabulary acquisition. While the student found listening to the EMI lectures challenging, he perceived the process as useful in preparing for university studies and a career as a secondary English teacher. The student perceived attention, topic, existing vocabulary knowledge, lecturer’s native language, and lack of interaction with the lecturer to have moderated incidental learning of vocabulary through listening to English lectures. These results indicate a need to confirm whether incoming students’ vocabulary knowledge meet the lexical demands of the EMI lectures given in the Macau context. Furthermore, pedagogical training on teacher talk strategies and orientation training for incoming students should both be provided to ensure students are receiving high quality instruction.

The effect of lexical coverage on L2 learners’ reading comprehension of narrative and expository genres

Understanding the relationship between lexical coverage (i.e., the number of words known in a text) and second language (L2) reading comprehension is pivotal to L2 instruction as it helps teachers set sensible vocabulary learning goals and select suitable reading materials. This study examined the unique contribution of 7 lexical coverage figures (i.e., 90%, 95%, 96%, 97%, 98%, 99%, and 100%) to the comprehension of narrative and expository texts read by Chinese EFL undergraduate students (N = 234). Participants completed the Vocabulary Size Test, a L2 reading ability test, a background knowledge test, reading comprehension tests, and topic familiarity ratings. Results of hierarchical regression analyses showed that after accounting for L2 vocabulary size and L2 reading ability, lexical coverage remained a steady, though weak, predictor of narrative and expository comprehension scores. No indication of a lexical threshold for reading comprehension was found. Instead, lexical coverage was found to be a stronger predictor of expository comprehension scores than narrative. While findings suggested 98% lexical coverage was reasonable for satisfactory comprehension of L2 narrative texts, 100% lexical coverage may be needed for satisfactory comprehension of L2 expository texts without the support of background knowledge. Pedagogical implications were discussed in relation to the findings.

Evaluating multiword unit word lists for academic purposes

Abstract Word lists play a critical role in English for Academic Purposes (EAP) teaching and learning, and recent developments include lists of academic collocations (e.g., vast majority, ultimate goal). There is however still a gap in evaluating lists focusing on a similar group of lexis. This paper evaluates two lists of academic collocations by Ackermann and Chen (2013) and Lei and Liu (2018) using three different methods: applying an evaluation framework adapted from Nation (2016), comparing the lexical constituents, and analysing the lexical coverage. The evaluation results give implications for EAP teachers to select the list that best suits their needs. By modelling the practice of evaluating word lists, this study highlights the importance of this work and encourages similar attempts in wordlist development studies.

The Relationship Between Lexical Coverage and Type of Reading Comprehension in Beginning L2 Spanish Learners

AbstractThis study examined the relationship between lexical coverage (i.e., the percentage of known word tokens in a text) and second language (L2) reading comprehension. Several studies have suggested that adequate comprehension occurs between 95% and 98% coverage, but no study has investigated beginning‐level learners reading under the conditions of extensive reading. Therefore, the present study recruited 50 native English‐speaking learners enrolled in a second‐semester Spanish course. Learners chose between 1 of 2 interesting, graded texts and read for general understanding. A yes–no vocabulary test was used to measure knowledge of all the words in the texts, and comprehension of the 10 main events was assessed with cued written recall and multiple‐choice tests. Additionally, to begin to understand how coverage relates to different types of comprehension, a literal and an inferential question was asked of each main event. The results indicated that (a) the relationship between lexical coverage and comprehension was moderate to strong, (b) lexical coverage was more associated with literal comprehension, and (c) 98% coverage would predict the productive recall of 8 of the 10 main events. The findings support the importance of having texts written at the lexical level of beginning L2 learners when engaging in extensive reading.

The Effects of Lexical Coverage and Topic Familiarity on the Comprehension of L2 Expository Texts

The Effects of Lexical Coverage and Topic Familiarity on the Comprehension of L2 Expository Texts

Academic lexical coverage in TED talks and academic lectures

The coverage of academic lexis is compared in a TED talk corpus (2,483 talks, 5,068,781 words) and a corpus of Yale University lectures (708 lectures, 5,523,791 words). Academic lexis is defined by the Academic Word List (Coxhead, 2000), the Academic Vocabulary List (Gardner & Davies, 2014), and the Academic Spoken Word List (Dang et al., 2017). In all cases Mann–Whitney U tests found lectures had significantly higher coverage, with small effect sizes for lexis. This difference was smaller for academic tagged TED talks (n = 1379). When like-for-like disciplines were compared, lectures typically had greater coverage than their TED talk counterparts. An analysis of the cumulative coverage of types demonstrated a lower representation of the less frequent academic types in TED talks. A combined ratio and minimum frequency measure identified academic types which distinguish the genres. Pedagogical implications are discussed.

Vocabulary in English Language Learning, Teaching, and Testing in Vietnam: A Review

This review paper aims to provide an overview of vocabulary in English language learning, teaching, and testing in Vietnam. First, we review studies on the vocabulary knowledge of Vietnamese EFL learners. Recent research evaluating different aspects of vocabulary knowledge shows that Vietnamese EFL learners generally have limited knowledge of both single words and formulaic language. Next, we discuss contemporary approaches to teaching vocabulary in Vietnam to reveal current issues and provide relevant recommendations. Empirical studies on Vietnamese EFL learners’ vocabulary acquisition are also discussed with an aim to shed light on how vocabulary can be acquired by Vietnamese EFL learners and subsequently draw important pedagogical implications. In addition, we look into the lexical component of high-stakes English tests in Vietnam, calling for more attention to the lexical profiles and lexical coverage of those tests. Finally, we provide concluding remarks and research-informed recommendations for EFL vocabulary learning and teaching in Vietnam to elaborate on how vocabulary can be effectively learned and taught.

A corpus-assisted analysis of the discursive construction of LGBT Singaporeans in media coverage of Pink Dot

Abstract Beginning in spring 2009 and continuing annually since, members of Singapore’s LGBT communities have assembled at Hong Lim Park at an event dubbed Pink Dot. The original goal of the gathering was to help build a more inclusive nation by standing up to discrimination faced by LGBT Singaporeans. While the early Pink Dot events were all but ignored by the mainstream state-run press, the change in tone, the increasing number of attendees, and the participation by members of the ruling People’s Action Party and their families made the gathering impossible to ignore. This paper uses a corpus-based keywords analysis to evaluate the main lexical differences between the media coverage of Pink Dot by the state-run press and that of the sociopolitical blog The Online Citizen. Two separate language corpora (State Media and Online Citizen), each containing approximately 111,000 words, were compiled from available coverage of Pink Dot dating from 2009 to 2018. Using SketchEngine (Kilgarriff et al. 2004, 2014), top keywords and phrases were identified by comparing these corpora to each other. Through a preliminary exploration of the collocational environments and the concordance lines adjoining these keywords, this paper sheds light on how language is being deployed in an attempt to sway a debate of great national and regional significance.

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.

GENERAL ENGLISH AND MORE OR LESS TECHNICAL ESP ON THE EXAMPLE OF WORD LISTS

The renewed interest in vocabulary research, especially due to its meaning-carrying significance in technical Englishes, has cleared the way to the development of corpus linguistics methods based on real-life texts. As the learning/teaching of general English leads towards the learning/teaching of English for a Specific Purpose (in line with the learner’s needs), so the creation of general English word lists imposes the creation of technical word lists, aiming to achieve a desired level of the target corpus comprehension. In this paper, we discuss the common grounds and specifics of various levels of general and technical English in terms of their lexical coverage of texts, specifically addressing the example of marine engineering technical manuals.

Lexical coverage in dialogue listening

In this quasi-experimental study, the effects of lexical coverage through pseudo word manipulation in dialogue comprehension are investigated. Forty-four first-year students in a Japanese university listened to five dialogues at different lexical coverage levels: 98%, 95%, 90%, 85%, and 83%. The results of the comprehension tests confirm the results seen in narrative, monologic lexical coverage studies that it is possible for intermediate EFL learners to attain adequate listening comprehension on texts with as little as 90% lexical coverage. However, variation in participants’ scores on higher lexical coverage dialogues suggest pseudo word distribution and topic familiarity might be acting as confounding variables in lexical coverage studies which use pseudo word manipulation. Suggestions for methodological reform for future projects on this subject are provided.

Lexical Coverage and Readability of Science Textbooks for English-Medium Instruction Secondary Schools in Hong Kong

Textbooks are an important source of knowledge input on which the transmission of academic knowledge often relies, especially in the early stages of academic learning. Adopting a corpus-based approach, this study evaluates the text difficulty of science textbooks used in secondary English-medium instruction schools in Hong Kong, with a focus on their lexical coverage and readability. It compares the English language used in English-medium science textbooks with that in English as a foreign language textbooks. The analysis reveals that the text difficulty of the English-medium science textbooks is inappropriate in terms of the coverage of academic words and the readability level. The results also show that the coverage of words from the Academic Word List (AWL) and the General Service List (GSL) as well as the text readability levels, vary across scientific topics. These findings will inform textbook design and the development of pedagogical strategies to facilitate students’ learning of subject content in the medium of English.

Word Frequency Level and Lexical Coverage in the Reading Comprehension Texts of the Malaysian University English Test

Word Frequency Level and Lexical Coverage in the Reading Comprehension Texts of the Malaysian University English Test

CAN TED TALK TRANSCRIPTS SERVE AS EXTENSIVE READING MATERIAL FOR MID-FREQUENCY VOCABULARY LEARNING?

Schmitt and Schmitt (2014) labeled the first 4000 to 9000 word families as mid-frequency words and stressed their importance based on Nation's (2006) estimate that for adequate comprehension of a variety of authentic texts, knowledge of the first 9000 word families is necessary. Subsequent to this vocabulary goal is to determine what can be read extensively to increase vocabulary progressively since most words cannot be mastered through only one exposure. This research aimed to investigate how much TED talk transcripts input is needed to encounter most of the first 9000 word families for learning to occur. It first measured the vocabulary levels of TED talks for their potential as extensive reading material for mid-frequency word learning. The results show that TED talks reached the 5th to 6th 1000-word-family level at 98% lexical coverage. Corpus sizes of 0.3 to 4.8 million words of TED transcripts provided an average of 12+ repetitions for most of the words from the first 4th to 9th 1000 word families. The figures may serve as a reference for learners in extensive reading programs to decide how much effort they should make to read TED talk transcripts voluminously to reach a certain vocabulary goal.

Advances in subword-based HMM-DNN speech recognition across languages

We describe a novel way to implement subword language models in speech recognition systems based on weighted finite state transducers, hidden Markov models, and deep neural networks. The acoustic models are built on graphemes in a way that no pronunciation dictionaries are needed, and they can be used together with any type of subword language model, including character models. The advantages of short subword units are good lexical coverage, reduced data sparsity, and avoiding vocabulary mismatches in adaptation. Moreover, constructing neural network language models (NNLMs) is more practical, because the input and output layers are small. We also propose methods for combining the benefits of different types of language model units by reconstructing and combining the recognition lattices. We present an extensive evaluation of various subword units on speech datasets of four languages: Finnish, Swedish, Arabic, and English. The results show that the benefits of short subwords are even more consistent with NNLMs than with traditional n-gram language models. Combination across different acoustic models and language models with various units improve the results further. For all the four datasets we obtain the best results published so far. Our approach performs well even for English, where the phoneme-based acoustic models and word-based language models typically dominate: The phoneme-based baseline performance can be reached and improved by 4% using graphemes only when several grapheme-based models are combined. Furthermore, combining both grapheme and phoneme models yields the state-of-the-art error rate of 15.9% for the MGB 2018 dev17b test. For all four languages we also show that the language models perform reasonably well when only limited training data is available.