Corpus-Based Approaches to Igbo Diacritic Restoration

• URL: http://arxiv.org/abs/2601.18380v1
• Date: Mon, 26 Jan 2026 11:30:36 GMT
• Title: Corpus-Based Approaches to Igbo Diacritic Restoration
• Authors: Ignatius Ezeani,
• Abstract summary: The capacity of computers to process natural languages is increasing because NLP researchers are pushing its boundaries.<n>Over 95% of the world's 7000 languages are low-resourced for NLP, i.e. they have little or no data, tools, and techniques for NLP work.<n>We present an overview of diacritic ambiguity and a review of previous diacritic disambiguation approaches on other languages.
• Score: 0.23552726065717702
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With natural language processing (NLP), researchers aim to enable computers to identify and understand patterns in human languages. This is often difficult because a language embeds many dynamic and varied properties in its syntax, pragmatics and phonology, which need to be captured and processed. The capacity of computers to process natural languages is increasing because NLP researchers are pushing its boundaries. But these research works focus more on well-resourced languages such as English, Japanese, German, French, Russian, Mandarin Chinese, etc. Over 95% of the world's 7000 languages are low-resourced for NLP, i.e. they have little or no data, tools, and techniques for NLP work. In this thesis, we present an overview of diacritic ambiguity and a review of previous diacritic disambiguation approaches on other languages. Focusing on the Igbo language, we report the steps taken to develop a flexible framework for generating datasets for diacritic restoration. Three main approaches, the standard n-gram model, the classification models and the embedding models were proposed. The standard n-gram models use a sequence of previous words to the target stripped word as key predictors of the correct variants. For the classification models, a window of words on both sides of the target stripped word was used. The embedding models compare the similarity scores of the combined context word embeddings and the embeddings of each of the candidate variant vectors.

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