Language Service Infrastructure on the Web: The Language Grid

Ishida, Toru; Murakami, Yohei; Lin, Donghui; Nakaguchi, Takao; Otani, Masayuki

doi:10.1109/mc.2018.2701643

Cited by 20 publications

(8 citation statements)

References 10 publications

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“…The Language Grid [27] was born from a long-term research project of intercultural collaboration in Kyoto University. The motivation was to provide the end users with an infrastructure that enables unhindered access to language resources and creation of customized multilingual environments.…”

Section: Language Service Infrastructure: the Language Gridmentioning

confidence: 99%

“…The Language Grid has been operating since 2007 from Kyoto University and, as of November 2019, has 183 participating groups from 24 countries sharing 226 language services. The Language Grid has been used for supporting various multilingual activities in hospital reception desks, local schools, shopping districts, international symposiums, and so on [27]. Research and development of the Language Grid have covered multiple areas, including artificial intelligence, NLP, services computing, and human-computer interaction [24,25].…”

Section: Applicability Of the Language Grid To Low-resource Languagesmentioning

confidence: 99%

“…To satisfy these infrastructure requirements, we developed the Language Grid, a service-oriented language infrastructure on the Web [24,25]. The Language Grid is built on service grid server software [26] and is based on the concepts of fragmentation, which provides various language services, and recombination, which realizes customizable language environments [27]. In fragmentation, existing language resources are wrapped using standardized interfaces to create atomic language services.…”

Section: Introductionmentioning

confidence: 99%

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Towards Language Service Creation and Customization for Low-Resource Languages

2020

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The most challenging issue with low-resource languages is the difficulty of obtaining enough language resources. In this paper, we propose a language service framework for low-resource languages that enables the automatic creation and customization of new resources from existing ones. To achieve this goal, we first introduce a service-oriented language infrastructure, the Language Grid; it realizes new language services by supporting the sharing and combining of language resources. We then show the applicability of the Language Grid to low-resource languages. Furthermore, we describe how we can now realize the automation and customization of language services. Finally, we illustrate our design concept by detailing a case study of automating and customizing bilingual dictionary induction for low-resource Turkic languages and Indonesian ethnic languages.

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Section: Language Service Infrastructure: the Language Gridmentioning

confidence: 99%

Section: Applicability Of the Language Grid To Low-resource Languagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards Language Service Creation and Customization for Low-Resource Languages

2020

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“…Layanan Google Translate dan layanan kamus dwibahasa dapat digabungkan menjadi layanan komposit pada Language Grid, sebuah media untuk berbagi sumber daya bahasa [5,6]. Language grid yang didirikan untuk membuat alat kolaborasi yang disesuaikan untuk mendukung masyarakat multibahasa, telah meningkatkan layanan berbasis web untuk masyarakat di seluruh dunia dengan menyediakan akses ke berbagai sumber daya bahasa dan juga menyediakan infrastruktur yang sangat mudah untuk diadaptasikan dengan sistem yang sudah tersedia [7]. Ada lebih dari seratus sumber daya bahasa yang tersedia di layanan Google Translate.…”

Section: Pivot-based Hybrid Machine Translationunclassified

Mesin Penerjemah Interaktif Dengan Animasi 3D Berbasis Augmented Reality

Nasution

Rizki²,

Nasution

et al. 2019

ITJRD

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Bahasa Inggris adalah salah satu bahasa yang digunakan sebagai alat komunikasi universal, karenanya tanpa kemampuan bahasa Inggris seorang akan mengalami kesulitan berkomunikasi secara baik dan benar pada ruang lingkup internasional. Penelitian ini mengembangkan aplikasi mesin penerjemah berbasis augmented reality yang dapat memberikan edukasi kepada siswa dengan media yang berbeda agar meningkatkan minat siswa dalam belajar bahasa Inggris. Aplikasi ini menggunakan library vuforia sdk yang mampu menampilkan karakter 3 dimensi dengan teknik markerless dalam bentuk augmented reality. Hasil akhir dari penelitian ini berupa aplikasi yang dapat digunakan pada smartphone dengan sistem operasi android, berdasar hasil pengujian terhadap aplikasi didapat kesimpulan bahwa aplikasi ini dapat menampilkan karakter 3 dimensi pada cahaya yang redup dengan intensitas cahaya 28 lux pada jarak 10cm-60 cm dan sudut penglihatan 10°-90°, setelah dilakukan peninjauan terhadap aplikasi 99% koresponden menyatakan aplikasi ini baik, maka aplikasi ini dapat membantu siswa mempelajari kembali bahasa Inggris selain disekolah.

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“…Nowadays, machine-readable bilingual dictionaries are being utilized in actual services [1] to support intercultural collaboration [2,3,4] and other research domains [5,6,7,8,9], but low-resource languages lack such sources. Indonesia has a population of 221,398,286 and 707 living languages which cover 57.8% of Austronesian Family and 30.7% of languages in Asia [10].…”

Section: Introductionmentioning

confidence: 99%

Generating similarity cluster of Indonesian languages with semi-supervised clustering

Nasution

Murakami

Ishida

2019

IJECE

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<p>Lexicostatistic and language similarity clusters are useful for computational linguistic researches that depends on language similarity or cognate recognition. Nevertheless, there are no published lexicostatistic/language similarity cluster of Indonesian ethnic languages available. We formulate an approach of creating language similarity clusters by utilizing ASJP database to generate the language similarity matrix, then generate the hierarchical clusters with complete linkage and mean linkage clustering, and further extract two stable clusters with high language similarities. We introduced an extended k-means clustering semi-supervised learning to evaluate the stability level of the hierarchical stable clusters being grouped together despite of changing the number of cluster. The higher the number of the trial, the more likely we can distinctly find the two hierarchical stable clusters in the generated k-clusters. However, for all five experiments, the stability level of the two hierarchical stable clusters is the highest on 5 clusters. Therefore, we take the 5 clusters as the best clusters of Indonesian ethnic languages. Finally, we plot the generated 5 clusters to a geographical map.</p>

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Language Service Infrastructure on the Web: The Language Grid

Cited by 20 publications

References 10 publications

Towards Language Service Creation and Customization for Low-Resource Languages

Towards Language Service Creation and Customization for Low-Resource Languages

Mesin Penerjemah Interaktif Dengan Animasi 3D Berbasis Augmented Reality

Generating similarity cluster of Indonesian languages with semi-supervised clustering

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