Automatic Restoration of Diacritics for Igbo Language

Ezeani, Ignatius; Hepple, Mark; Onyenwe, Ikechukwu

doi:10.1007/978-3-319-45510-5_23

Cited by 11 publications

(11 citation statements)

References 6 publications

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“…Our earlier work on Igbo diacritic restoration (Ezeani et al, 2016) was more of a proof of concept aimed at extending the initial work done by Scannell (2011). We built a number of n-gram models -basically unigrams, bigrams and trigrams -along with simple smoothing techniques.…”

Section: Grapheme or Letter Level Diacritic Restorationsupporting

confidence: 46%

Lexical Disambiguation of Igbo using Diacritic Restoration

Ezeani¹,

Hepple²,

Onyenwe³

2017

Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and Their Applications

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Properly written texts in Igbo, a low resource African language, are rich in both orthographic and tonal diacritics. Diacritics are essential in capturing the distinctions in pronunciation and meaning of words, as well as in lexical disambiguation. Unfortunately, most electronic texts in diacritic languages are written without diacritics. This makes diacritic restoration a necessary step in corpus building and language processing tasks for languages with diacritics. In our previous work, we built some n−gram models with simple smoothing techniques based on a closedworld assumption. However, as a classification task, diacritic restoration is well suited for and will be more generalisable with machine learning. This paper, therefore, presents a more standard approach to dealing with the task which involves the application of machine learning algorithms.

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Section: Grapheme or Letter Level Diacritic Restorationsupporting

confidence: 46%

Lexical Disambiguation of Igbo using Diacritic Restoration

Ezeani¹,

Hepple²,

Onyenwe³

2017

Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and Their Applications

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“…The results of our experiments are shown in the Tables 2, 3, 4, 5, 6, 8 3 . We did not use Word Error Rate (WER) and Diacritics Error rate (DER) since they are introduced as accuracy calculated for words and single diacritics, respectively.…”

Section: Results Of Experimentsmentioning

confidence: 99%

“…In Vietnamese and Igbo, there are words that differ in diacritics only, but have extremely different meanings. So, for example, the paper [3] reports the following chain: akwà (cloth),àkwà (bed/bridge),ákwá (cry),àkwá (egg).…”

Section: Overviewmentioning

confidence: 99%

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A Comparison of Neural Networks Architectures for Diacritics Restoration

Klyshinsky

Karpik

Bondarenko

2021

Communications in Computer and Information Science

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Neural networks are widely used for the task of diacritics restoration last years. Authors use different architectures of neural network for selected languages. In this paper, we demonstrated that an architecture should be selected according to a language in hand. It also depends on a task one states: low and full resourced languages could use different architectures. We demonstrated that common used accuracy metric should be changed in this task to precision and recall due to the heavy unbalanced nature of the input data. The paper contains results for seven languages: Croatian,

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“…Several other models used the lexical lookup (or dictionaries) as default and other methods to handle cases outside the dictionaries. A similar approach was taken in Ezeani, Hepple and Onyenwe (2016) in the diacritic restoration of Igbo with backward replacement that could overwrite the diacritic form predicted for previous word if the bigram strongly suggest a better replacement.…”

Section: Data-driven Diacritization Methodsmentioning

confidence: 97%

A survey of diacritic restoration in abjad and alphabet writing systems

2017

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A diacritic is a mark placed near or through a character to alter its original phonetic or orthographic value. Many languages around the world use diacritics in their orthography, whatever the writing system the orthography is based on. In many languages, diacritics are ignored either by convention or as a matter of convenience. For users who are not familiar with the text domain, the absence of diacritics within text has been known to cause mild to serious readability and comprehension problems. However, the absence of diacritics in text causes near-intractable problems for natural language processing systems. This situation has led to extensive research on diacritization. Several techniques have been applied to address diacritic restoration (or diacritization) but the existing surveys of techniques have been restricted to some languages and hence left gaps for practitioners to fill. Our survey examined diacritization from the angle of resources deployed and various formulation employed for diacritization. It was concluded by recommending that (a) any proposed technique for diacritization should consider the language features and the purpose served by diacritics, (b) that evaluation metrics needed to be more rigorously defined for easy comparison of performance of models.

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Automatic Restoration of Diacritics for Igbo Language

Cited by 11 publications

References 6 publications

Lexical Disambiguation of Igbo using Diacritic Restoration

Lexical Disambiguation of Igbo using Diacritic Restoration

A Comparison of Neural Networks Architectures for Diacritics Restoration

A survey of diacritic restoration in abjad and alphabet writing systems

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