@InProceedings{ezeani-hepple-onyenwe:2017:SENSE2017,
  author    = {Ezeani, Ignatius  and  Hepple, Mark  and  Onyenwe, Ikechukwu},
  title     = {Lexical Disambiguation of Igbo using Diacritic Restoration},
  booktitle = {Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {53--60},
  abstract  = {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 closed-world 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.},
  url       = {http://www.aclweb.org/anthology/W17-1907}
}