MulTINCo: multilingual traditional immersion and native corpus. Better-documented multiliteracy practices for more refined SLA studies

Meunier, Fanny; Hendrikx, Isa; Bulon, Amélie; Goethem, Kristel Van; Naets, Hubert

doi:10.1080/13670050.2020.1786494

Cited by 4 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the framework of a project on CLIL (Van Mensel & Hiligsmann, 2020), spoken data from 64 students in the 6th grade (mean age: 17,33 years old) from three different secondary schools spread over French-speaking Belgium was collected in 2017 and is stored in the MulTINCo database (Meunier et al, 2020).4 The entire dataset represents the speech of 38 girls and 26 boys whose ages ranged from 15 to 18, in the form of 16 English conversations, 17 Dutch conversations, and 32 French conversations. The participants were asked to talk in pairs about two different topics (holidays and a party).…”

Section: Methodsmentioning

confidence: 99%

“…However, we do not take into account this distinction because it would make the samples too small. Note that the samples are mixed CLIL/non-CLIL for both AL s. 5 More information about the database is available in Meunier et al (2020). 6 https://lands.let.ru.nl/cgn/ 7 https://www.um.es/sacodeyl/ 8 The design of the CGN did not allow for a search in terms of specific text samples with a definite word count.…”

Section: Author Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Intensification strength in speech: language-specific preferences and differences between native and learner language

Hendrikx,

Van Goethem,

Buntinx

2024

DuJAL

Self Cite

View full text Add to dashboard Cite

In this article we analyse the use of intensification in the spoken productions of French-speaking learners of Dutch and English. We compare the strength of intensifiers used by learners in their first language (L1) and in their additional language (AL), and contrast these results with data from control groups of L1 speakers. Our corpus results indicate that L1 English speakers tend to intensify more frequently but opt for weaker intensifiers, while L1 French speakers intensify less frequently but use stronger intensifiers. L1 Dutch speakers take the middle position in both aspects. The analysis of the learner corpora reveals overall more similarities between AL English and L1 English than between AL Dutch and L1 Dutch, confirming the trends observed in previous studies on the same learners (Hendrikx, 2019).

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Author Contributionsmentioning

confidence: 99%

Intensification strength in speech: language-specific preferences and differences between native and learner language

Hendrikx,

Van Goethem,

Buntinx

2024

DuJAL

Self Cite

View full text Add to dashboard Cite

show abstract

A Native Video Gamer's Journey Toward Multi-literacy Development: A Narrative Inquiry

Ebadi

Ahmadi

2022

Journal of Language, Identity & Education

View full text Add to dashboard Cite

SPECIL: Spell Error Corpus for the Indonesian Language

Yanfi,

Setiawan,

Soeparno

et al. 2023

IEEE Access

View full text Add to dashboard Cite

In this study, we present the first spell error corpus for the Indonesian Language (SPECIL). This corpus provides a comprehensive resource for researchers and practitioners to detect and correct spelling errors in Bahasa Indonesia (Indonesian). It should be emphasized that currently, there is no recognized corpus for identifying spelling mistakes in the Indonesian language that has been officially released or made accessible. This study also provides a systematic literature review to identify resources and methodologies for building a corpus for spelling error detection and correction in Indonesia. A corpus was created using a combination of manual and automatic methods. The results of this study are a review of publications relating to corpora and spelling, the novel algorithm of six types of spelling errors, and the production of a corpus comprising over 180,000 tokens in 21,500 sentences, including non-word, real-word, and punctuation errors. Using the developed corpus, various Natural Language Processing (NLP) models, including spell checkers and language models, can be trained and tested to enhance their accuracy and effectiveness in identifying and rectifying errors in Indonesian texts. Moreover, the corpus can be used to develop and evaluate new algorithms and techniques for spelling error detection and correction in Indonesia. The SPECIL corpus is publicly available and accessible. It is expected that SPECIL will inspire further research in this area and facilitate the development of more accurate and effective spelling error detection and correction tools in Indonesian language.

show abstract

MulTINCo: multilingual traditional immersion and native corpus. Better-documented multiliteracy practices for more refined SLA studies

Cited by 4 publications

References 31 publications

Intensification strength in speech: language-specific preferences and differences between native and learner language

Intensification strength in speech: language-specific preferences and differences between native and learner language

A Native Video Gamer's Journey Toward Multi-literacy Development: A Narrative Inquiry

SPECIL: Spell Error Corpus for the Indonesian Language

Contact Info

Product

Resources

About