Name
Abstract | ||
|---|---|---|
The orthography of many resource-scarce languages includes diacritically marked characters. Falling outside the scope of the standard Latin encoding, these characters are often represented in digital language resources as their unmarked equivalents. This renders corpus compilation more difficult, as these languages typically do not have the benefit of large electronic dictionaries to perform diacritic restoration. This paper describes experiments with a machine learning approach that is able to automatically restore diacritics on the basis of local graphemic context. We apply the method to the African languages of Cilubà, Gikuyu, Kikamba, Maa, Sesotho sa Leboa, Tshivenda and Yoruba and contrast it with experiments on Czech, Dutch, French, German and Romanian, as well as Vietnamese and Chinese Pinyin. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74628-7_24 | TSD |
Keywords | DocType | Volume |
chinese pinyin,automatic diacritic restoration,digital language resource,sesotho sa leboa,african language,local graphemic context,large electronic dictionary,resource-scarce language,diacritic restoration,diacritically marked character,renders corpus compilation,machine learning | Conference | 4629 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-74627-7 | 8 |
PageRank | References | Authors |
0.82 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guy Pauw | 1 | 75 | 12.47 |
| Peter W. Wagacha | 2 | 12 | 2.95 |
| Gilles-Maurice Schryver | 3 | 17 | 2.17 |