Natural language processing for resource-poor languages

Natural language processing (NLP) aims, broadly speaking, to teach computers to understand human language. This is hard as the computer must comprehend many facets of language such as semantics, syntax, pragmatics and phonology which are difficult to characterize formally, let alone encode as computer instructions. It is even harder for so-called low-resource languages where the annotated resources are very limited. There are approximately 7,000 languages in the world, but of these only a small fraction (20 languages) are considered high-resource languages. Low-resource languages are in dire need of tools and resources to overcome the resource barrier such that advances in NLP can deliver more widespread benefits. Despite the lack of annotated data, there are some unannotated data resources which might be beneficial for low-resource languages including parallel data, bilingual lexical resources or clues from related languages. However, the means for effectively incorporating these resources to improve the performance of low-resource NLP is an open research question, and the target of this thesis. Out of 7,000 languages, half of them do not have a writing system and many are falling out of use. It is estimated that by the end of this century, half of the world’s languages will be extinct. It is necessary to extend the current NLP techniques to unwritten languages to process and document the languages before they are gone forever. Transfer learning provides an important opportunity for low-resource NLP, whereby annotation is transferred from a source resource-rich language to a target resource poor-language. In this thesis, we successfully apply transfer learning for

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