MāOri Loanwords: A Corpus of New Zealand English Tweets

Trye, David; Calude, Andreea S.; Bravo-Márquez, Felipe; Keegan, Te Taka Adrian Gregory

doi:10.18653/v1/p19-2018

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…Grieve et al, 2017 ). This process is briefly summarized below, but a more detailed explanation is given in Trye et al ( 2019 ).…”

Section: Methodsmentioning

confidence: 99%

“…Drawing on lessons learned from the original study (Trye et al, 2019 ), some improvements were made to further mitigate noise in the MLT corpus. First, the corpus was enhanced by deploying a Multinomial Naive Bayes model (McCallum and Nigam, 1998 ) that considered not only unigrams in the feature space (as per the previous study), but bigrams as well.…”

Section: Methodsmentioning

confidence: 99%

“…The study of Māori loanwords in New Zealand English has received intense scrutiny in the literature, especially with regard to newspaper language (Davies and MacLagan, 2006 ; Macalister, 2006 , 2009 ; Onysko and Calude, 2013 ), Hansard Parliament debates (Macalister, 2006 ), children's picture books (Daly, 2007 , 2016 ), TV language (de Bres, 2006 ), conversation (Kennedy and Yamazaki, 1999 ), and more recently, online science discourse (Calude et al, 2019b ). However, very little is known about the use of Māori loanwords on social media (with the exception of a small sample of tweets in Calude et al, 2019b , and preliminary findings in Trye et al, 2019 ), which motivates our attention to Twitter data here.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English

Trye

Calude

Bravo-Márquez

et al. 2020

Front. Artif. Intell.

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Twitter constitutes a rich resource for investigating language contact phenomena. In this paper, we report findings from the analysis of a large-scale diachronic corpus of over one million tweets, containing loanwords from te reo Māori, the indigenous language spoken in New Zealand, into (primarily, New Zealand) English. Our analysis focuses on hashtags comprising mixed-language resources (which we term hybrid hashtags), bringing together descriptive linguistic tools (investigating length, word class, and semantic domains of the hashtags) and quantitative methods (Random Forests and regression analysis). Our work has implications for language change and the study of loanwords (we argue that hybrid hashtags can be linked to loanword entrenchment), and for the study of language on social media (we challenge proposals of hashtags as "words," and show that hashtags have a dual discourse role: a micro-function within the immediate linguistic context in which they occur and a macro-function within the tweet as a whole).

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“…Grieve et al, 2017 ). This process is briefly summarized below, but a more detailed explanation is given in Trye et al ( 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English

Trye

Calude

Bravo-Márquez

et al. 2020

Front. Artif. Intell.

Self Cite

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“…Finally, it should be noted that, at the time of writing this paper, Modern Greek data sets and text mining approaches are certainly fewer in number when compared against works focusing on other, more widely spoken, popular languages, such as English, German, Spanish, French, Italian, Russian or Chinese. In general, when compared to corresponding tasks in other languages, the obtained accuracy results in Modern Greek web text mining tasks indicate a better overall performance than [50][51][52][53][54][55][56][57], comparable to [58][59][60], or worse than [61][62][63].…”

Section: Approaches On Developing Modern Greek Social Web Text Data Sets and On Modernmentioning

confidence: 98%

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Nikiforos

Voutos

Drougani

et al. 2021

Data

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Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.

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“…The RMT Corpus complements this existing body of Ma ¯ori-language text since, although a mixed-language corpus of three million English tweets exists that contains borrowed Ma ¯ori words or loanwords (Trye et al, 2019(Trye et al, , 2020, there is, to the best of our knowledge, no social media corpus comprising (almost) exclusively Ma ¯ori-language text.…”

Section: Existing Ma ¯Ori-language Resourcesmentioning

confidence: 99%

Harnessing Indigenous Tweets: The Reo Māori Twitter corpus

Trye

Keegan

Mato

et al. 2022

Lang Resources & Evaluation

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Te reo Māori, the Indigenous language of Aotearoa New Zealand, is a distinctive feature of the nation’s cultural heritage. This paper documents our efforts to build a corpus of 79,000 Māori-language tweets using computational methods. The Reo Māori Twitter (RMT) Corpus was created by targeting Māori-language users identified by the Indigenous Tweets website, pre-processing their data and filtering out non-Māori tweets, together with other sources of noise. Our motivation for creating such a resource is three-fold: (1) it serves as a rich and unique dataset for linguistic analysis of te reo Māori on social media; (2) it can be used as training data to develop and augment Natural Language Processing (NLP) tools with robust, real-world Māori-language applications; and (3) it will potentially promote awareness of, and encourage positive interaction with, the growing community of Māori tweeters, thereby increasing the use and visibility of te reo Māori in an online environment. While the corpus captures data from 2007 to 2020, our analysis shows that the number of tweets in the RMT Corpus peaked in 2014, and the number of active tweeters peaked in 2017, although at least 600 users were still active in 2020. To the best of our knowledge, the RMT Corpus is the largest publicly-available collection of social media data containing (almost) exclusively Māori text, making it a useful resource for language experts, NLP developers and Indigenous researchers alike.

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MāOri Loanwords: A Corpus of New Zealand English Tweets

Cited by 7 publications

References 19 publications

Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English

Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Harnessing Indigenous Tweets: The Reo Māori Twitter corpus

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