Using key part-of-speech analysis to examine spoken discourse by Taiwanese EFL learners

Abstract

This study reports on a corpus analysis of samples of spoken discourse between a group of British and Taiwanese adolescents, with the aim of exploring the statistically significant differences in the use of grammatical categories between the two groups of participants. The key word method extended to a part-of-speech level using the web-based corpus analytical tool, Wmatrix, highlights those linguistic domains which deserve particular attention. Specifically, it reveals the lexical and grammatical categories that occur unusually frequently or unusually infrequently in the English learners’ discourse when compared with the language used by the native speakers of English in the sample. The research findings delineate the pedagogical merit of key domain analysis and thus help to inform English as a foreign language teachers and materials developers in the design of courses emphasising spoken interaction.

Classifying vocabulary in Google Sheets to improve word recognition and reading comprehension in EFL learners: an action research study

“…Raising awareness of the function of words in context through different activities proved to improve their vocabulary size and depth. This improvement can also be related to the connection of how native speakers use vocabulary in the language, in this case in readings, as Lin (2014) pointed out.…”

“…Melinger and Koenig (2007) suggested that the lexical organization is done according to the syntactic dimensions of part-of-speech. According to Lin (2014), the analysis of parts of speech provides English learners an insight into how native English speakers use lexical resources in the language. The four basic parts of speech are the classes of nouns, verbs, adjectives, and adverbs.…”

Contrastive analysis of adolescent learner interlanguage in asynchronous online communication: A keyness approach

“…It is thus suited to highlight the main elements that are characteristic of a specific collection of texts. Keyness analysis works by comparing the actual observed frequency of each item in the target corpus with its equivalent in the reference corpus (Adolphs, 2006;Lin, 2015;Baker, 2006;Bondi & Scott, 2010;Scott & Tribble, 2006). In this regard, patterns of over-and underuse of linguistic features can be identified and provide impetus for further analysis (Granger, 2015).…”

“…First, it reveals a number of cultural and social differences in language use, as well as learner errors with regard to grammar. As Granger (2015) claims, "overuse is often an indicator of error" (p.11), and EFL teachers can therefore encourage their learners to observe these significant differences in the use POS categories (Lin, 2015;MacDonald et al, 2013). Second, by using automatic tagging software that assigns POS tags, macroscopic analysis can help to identify those linguistic features that deserve further attention, providing a reasoned basis for drawing learners' awareness to linguistic features specific to their own and the native-speaker discourse (Rayson, 2008;Tribble, 2000).…”

What effect does short term Study Abroad (SA) have on learners’ vocabulary knowledge?

“…Finally, the SA data will be subdivided into words produced at specific time points to see if any significant changes in the frequency of occurrence of individual words, Part Of Speech (POS) groups and semantic domains, occur over the course of a short-term SA experience. Where evidence of such changes exist an attempt will be made to compare them with the findings of earlier studies (Lin, 2014;Lin, 2017) and to explain them within the framework of a typical SA programme. This study will allow us to test our prediction that the application of Wmatrix to SA data may help us to identify certain specific characteristics in the kind of language SA participants produce and how these might change over time.…”