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This paper aims to investigate how Bruneian secondary school students employ code-switching in peer interactions. The functions of students' code-switching were analysed using Reyes' (2004) and Appel and Muysken's (2005) typologies.

The data collected are based on audio-recorded group discussions designed to elicit students’ code-switched utterances.

The results indicate that the students used 11 functions of code-switching: referential, discourse marker, clarification, expressive, quotation imitation, turn accommodation, insistence, emphasis, question shift, situation shift and poetic.

As the study only focusses on a specific secondary school, results from this school will not represent secondary school students in Brunei.

This paper hopes to provide insight into how students' code-switching can be seen in a positive light. Moreover, understanding how students use code-switching in the classroom is essential for successful knowledge transfer and for cultivating competent bilinguals, which is what the country's education system aims for.

Purpose: This chapter analyses the effects of social stratification and inequalities on the outcomes of transnational mobilities, especially on the educational trajectory of returning migrant children.

Study approach: It places the Bourdieusian capital concepts (Bourdieu, 1977, 1984) centre stage, and analyses the convertibility or transferability of the cultural and social capital across different transnational locations. It examines the serious limitations of this process, using the concept of non-dominant cultural capital as a heuristic analytical tool and the education system (school) as a way of approaching the field. As we examine ‘successful mobilities’ of high-status families with children and racialised low-status families experiencing mobility failures, our intention is to draw attention on the effect of the starting position of the migrating families on the outcomes of their cross-border mobilities through a closer reading of insightful cases. We look at the interrelations of social position or class race and mobility experiences through several empirical case studies from different regions of Hungary by examining the narratives of people belonging to very different social strata with a focus on the ‘top’ and the ‘bottom’ of the socio-economic hierarchy. We examine the transnational mobility trajectories, strategies and the reintegration of school age children from transnationally mobile families upon their return to Hungary.

Findings: Our qualitative research indicates that for returning migrants not only their available capitals in a Bourdieasian sense but also their (de)valuation by the different Hungarian schools has direct consequences on mobility-affected educational trajectories, on the individual outcomes of mobilities, and the circumstances of return and chances for reintegration.

Originality: There is little qualitative research on the effects of emigration from Hungary in recent decades. A more recent edited volume (Váradi, 2018) discusses various intersectionalities of migration such as gender, ethnicity and age. This chapter intends to advance this line of research, analysing the intersectionality of class, ethnicity and race in the context of spatial mobilities through operationalising a critical reading of the Bourdieusian capitals.

Clinical notes typically contain medical jargons and specialized words and phrases that are complicated and technical to most people, which is one of the most challenging obstacles in health information dissemination to consumers by healthcare providers. The authors aim to investigate how to leverage machine learning techniques to transform clinical notes of interest into understandable expressions.

The authors propose a natural language processing pipeline that is capable of extracting relevant information from long unstructured clinical notes and simplifying lexicons by replacing medical jargons and technical terms. Particularly, the authors develop an unsupervised keywords matching method to extract relevant information from clinical notes. To automatically evaluate completeness of the extracted information, the authors perform a multi-label classification task on the relevant texts. To simplify lexicons in the relevant text, the authors identify complex words using a sequence labeler and leverage transformer models to generate candidate words for substitution. The authors validate the proposed pipeline using 58,167 discharge summaries from critical care services.

The results show that the proposed pipeline can identify relevant information with high completeness and simplify complex expressions in clinical notes so that the converted notes have a high level of readability but a low degree of meaning change.

The proposed pipeline can help healthcare consumers well understand their medical information and therefore strengthen communications between healthcare providers and consumers for better care.

An innovative pipeline approach is developed to address the health literacy problem confronted by healthcare providers and consumers in the ongoing digital transformation process in the healthcare industry.

Recently, more data-driven approaches are demanding multilingual parallel resources primarily in the cross-language studies. To meet these demands, building multilingual parallel corpora are becoming the focus of many Natural Language Processing (NLP) scientific groups. Unlike monolingual corpora, the number of available multilingual parallel corpora is limited. In this paper, the MulTed, a corpus of subtitles extracted from TEDx talks is introduced. It is multilingual, Part of Speech (PoS) tagged, and bilingually sentence-aligned with English as a pivot language. This corpus is designed for many NLP applications, where the sentence-alignment, the PoS tagging, and the size of corpora are influential such as statistical machine translation, language recognition, and bilingual dictionary generation. Currently, the corpus has subtitles that cover 1100 talks available in over 100 languages. The subtitles are classified based on a variety of topics such as Business, Education, and Sport. Regarding the PoS tagging, the Treetagger, a language-independent PoS tagger, is used; then, to make the PoS tagging maximally useful, a mapping process to a universal common tagset is performed. Finally, we believe that making the MulTed corpus available for a public use can be a significant contribution to the literature of NLP and corpus linguistics, especially for under-resourced languages.

Investigating technical terms of vehicle spare parts used in the mechanics' jargon in Saudi Arabic (SA) and Yemeni Arabic (YA) has received scant attention. The current study, therefore, is an attempt to shed some light on the topic. The aim is to identify the strategies used for creating equivalents in vehicle spare parts vocabulary and to pinpoint the most salient variations between the two dialects in this jargon.

More than 250 terms of vehicle spare parts were collected and analyzed qualitatively. Each list contains nearly 125 items. They were gathered from two main resources: semi-structured interviews with vehicle mechanics, and written lists from spare parts dealers in both countries.

Three main strategies are found at work: lexical borrowing (from English and French), metaphor and loan translation. Direct borrowing is the most influential strategy where loanwords represent nearly one-third of the data, the majority of which is from English. Metaphorical extensions and literal translations also have an important role to play in the process of spare part naming. While the two dialects share common practices in terms of literal translation, they are characterized by many differences with regard to lexical borrowing and metaphors.

The study approaches an under-researched topic that is related to the mechanic's jargon in Arabic and leaves the door open for further research. The findings of this study may be used as guidelines for Arabic academies and those who are concerned with translating and studying technical terms in the field of mechanical engineering.

Using a newly compiled corpus module consisting of utterances from Asian learners during L2 English interviews, this study examined how Asian EFL learners' L1s (Chinese, Indonesian, Japanese, Korean, Taiwanese and Thai), their L2 proficiency levels (A2, B1 low, B1 upper and B2+) and speech task types (picture descriptions, roleplays and QA-based conversations) affected four aspects of vocabulary usage (number of tokens, standardized type/token ratio, mean word length and mean sentence length).

Four aspects concern speech fluency, lexical richness, lexical complexity and structural complexity, respectively.

Subsequent corpus-based quantitative data analyses revealed that (1) learner/native speaker differences existed during the conversation and roleplay tasks in terms of the number of tokens, type/token ratio and sentence length; (2) an L1 group effect existed in all three task types in terms of the number of tokens and sentence length; (3) an L2 proficiency effect existed in all three task types in terms of the number of tokens, type-token ratio and sentence length; and (4) the usage of high-frequency vocabulary was influenced more strongly by the task type and it was classified into four types: Type A vocabulary for grammar control, Type B vocabulary for speech maintenance, Type C vocabulary for negotiation and persuasion and Type D vocabulary for novice learners.

These findings provide clues for better understanding L2 English vocabulary usage among Asian learners during speech.

This paper aims to use an analytic hierarchy process (AHP) and combine this with the fuzzy theory to identify key indicators influencing English-medium instruction (EMI) in the shipping courses of Taiwan’s higher education.

Based on a literature review and expert interviews, an evaluation model with 4 indicators and 13 sub-indicators was developed. Questionnaire samples included university English teachers (eight), university shipping teachers (nine) and shipping practitioners (eight).

Using 25 effective samples, the results found that “teachers’ characteristics” is the most important indicator, followed by “syllabus design”, “university resources” and “students’ characteristics”. Such a finding could provide valuable teaching and managerial strategies for EMI design in both university and industry sectors.

Expert questionnaire targets have focused on university English teachers, university shipping teachers and shipping practitioners. Other related field experts could be further surveyed and compared in the future studies.

The findings of EMI indicators in the shipping courses could be used for course and material design by shipping companies, shipping authorities and universities. It is expected that these indicators could inform the provision of reasonable teaching resources allocation.

This paper provides important guidance for designing EMI in shipping courses. Related stakeholders will be able to understand important concepts regarding designing EMI courses.

First, EMI indicators in the shipping courses have seldom been studied in the past. They are, however, important for both shipping industries and education intuitions. Second, as its method, this paper adopts decision analysis quantitative tool to complement previous qualitative studies regarding EMI studies.