Towards Language Service Creation and Customization for LowResource Languages The most challenging issue with lowresource languages is the difficulty of obtaining enough language resources. In this paper, we propose a language service framework for lowresource languages that enables the automatic creation and customization of new resources from existing ones. To achieve this goal, we first introduce a serviceoriented 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 lowresource 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 lowresource Turkic languages and Indonesian ethnic languages.

Viability of Neural Networks for Core Technologies for ResourceScarce Languages In this paper, the viability of neural network implementations of core technologies (the focus of this paper is on text technologies) for 10 resourcescarce South African languages is evaluated. Neural networks are increasingly being used in place of other machine learning methods for many natural language processing tasks with good results. However, in the South African context, where most languages are resourcescarce, very little research has been done on neural network implementations of core language technologies. In this paper, we address this gap by evaluating neural network implementations of four core technologies for ten South African languages. The technologies we address are part of speech tagging, named entity recognition, compound analysis and lemmatization. Neural architectures that performed well on similar tasks in other settings were implemented for each task and the performance was assessed in comparison with currently used machine learning implementations of each technology. The neural network models evaluated perform better than the baselines for compound analysis, are viable and comparable to the baseline on most languages for POS tagging and NER, and are viable, but not on par with the baseline, for Afrikaans lemmatization.