Te Taka Keegan


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A Hybrid Architecture for Labelling Bilingual Māori-English Tweets
David Trye | Vithya Yogarajan | Jemma König | Te Taka Keegan | David Bainbridge | Mark Apperley
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022

Most large-scale language detection tools perform poorly at identifying Māori text. Moreover, rule-based and machine learning-based techniques devised specifically for the Māori-English language pair struggle with interlingual homographs. We develop a hybrid architecture that couples Māori-language orthography with machine learning models in order to annotate mixed Māori-English text. This architecture is used to label a new bilingual Twitter corpus at both the token (word) and tweet (sentence) levels. We use the collected tweets to show that the hybrid approach outperforms existing systems with respect to language detection of interlingual homographs and overall accuracy. We also evaluate its performance on out-of-domain data. Two interactive visualisations are provided for exploring the Twitter corpus and comparing errors across the new and existing techniques. The architecture code and visualisations are available online, and the corpus is available on request.


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Māori Loanwords: A Corpus of New Zealand English Tweets
David Trye | Andreea Calude | Felipe Bravo-Marquez | Te Taka Keegan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Māori loanwords are widely used in New Zealand English for various social functions by New Zealanders within and outside of the Māori community. Motivated by the lack of linguistic resources for studying how Māori loanwords are used in social media, we present a new corpus of New Zealand English tweets. We collected tweets containing selected Māori words that are likely to be known by New Zealanders who do not speak Māori. Since over 30% of these words turned out to be irrelevant, we manually annotated a sample of our tweets into relevant and irrelevant categories. This data was used to train machine learning models to automatically filter out irrelevant tweets.


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Minority languages & translation technologies case study: te reo Māori & Google Translator Toolkit
Te Taka Keegan | Hōri Manuirirangi
Proceedings of Translating and the Computer 33

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A word-based approach for diacritic restoration in Māori
John Cocks | Te Taka Keegan
Proceedings of the Australasian Language Technology Association Workshop 2011