6 research outputs found

    Diacritic Restoration and the Development of a Part-of-Speech Tagset for the Māori Language

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    This thesis investigates two fundamental problems in natural language processing: diacritic restoration and part-of-speech tagging. Over the past three decades, statistical approaches to diacritic restoration and part-of-speech tagging have grown in interest as a consequence of the increasing availability of manually annotated training data in major languages such as English and French. However, these approaches are not practical for most minority languages, where appropriate training data is either non-existent or not publically available. Furthermore, before developing a part-of-speech tagging system, a suitable tagset is required for that language. In this thesis, we make the following contributions to bridge this gap: Firstly, we propose a method for diacritic restoration based on naive Bayes classifiers that act at word-level. Classifications are based on a rich set of features, extracted automatically from training data in the form of diacritically marked text. This method requires no additional resources, which makes it language independent. The algorithm was evaluated on one language, namely Māori, and an accuracy exceeding 99% was observed. Secondly, we present our work on creating one of the necessary resources for the development of a part-of-speech tagging system in Māori, that of a suitable tagset. The tagset described was developed in accordance with the EAGLES guidelines for morphosyntactic annotation of corpora, and was the result of in-depth analysis of the Māori grammar

    Spell-checking in Spanish: the case of diacritic accents

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    This article presents the problem of diacritic restoration (or diacritization) in the context of spell-checking, with the focus on an orthographically rich language such as Spanish. We argue that despite the large volume of work published on the topic of diacritization, currently available spell-checking tools have still not found a proper solution to the problem in those cases where both forms of a word are listed in the checker’s dictionary. This is the case, for instance, when a word form exists with and without diacritics, such as continuo ‘continuous’ and continuĂł ‘he/she/it continued’, or when different diacritics make other word distinctions, as in continĂșo ‘I continue’. We propose a very simple solution based on a word bigram model derived from correctly typed Spanish texts and evaluate the ability of this model to restore diacritics in artificial as well as real errors. The case of diacritics is only meant to be an example of the possible applications for this idea, yet we believe that the same method could be applied to other kinds of orthographic or even grammatical errors. Moreover, given that no explicit linguistic knowledge is required, the proposed model can be used with other languages provided that a large normative corpus is available.Peer ReviewedPostprint (author’s final draft

    On reconstructing Proto-Bantu grammar

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    This book is about reconstructing the grammar of Proto-Bantu, the ancestral language at the origin of current-day Bantu languages. While Bantu is a low-level branch of Niger-Congo, the world’s biggest phylum, it is still Africa’s biggest language family. This edited volume attempts to retrieve the phonology, morphology and syntax used by the earliest Bantu speakers to communicate with each other, discusses methods to do so, and looks at issues raised by these academic endeavours. It is a collective effort involving a fine mix of junior and senior scholars representing several generations of expert historical-comparative Bantu research. It is the first systematic approach to Proto-Bantu grammar since Meeussen’s Bantu Grammatical Reconstructions (1967). Based on new bodies of evidence from the last five decades, most notably from northwestern Bantu languages, this book considerably transforms our understanding of Proto-Bantu grammar and offers new methodological approaches to Bantu grammatical reconstruction
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