Using machine learning to build POS tagger for under-resourced language: the case of Somali

Mohammed, Siraj

doi:10.1007/s41870-020-00480-2

Cited by 14 publications

(9 citation statements)

References 6 publications

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“…Finally, it is decided that every remaining unknown (UNK) word is annotated as noun (N). This result confirms the findings in [4,11,21], which were proposed for similiar language family like afaan Oromo and Somali language.…”

Section: B Algorithm For Implementing Hmmsupporting

confidence: 92%

“…Although, it was proposed to develop POS tagging model for resource rich languages like English and French in many research works [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], much less attention was given to under-resource languages like Shekkinoono. And several methodologies were explored to develop part of speech tagging for Shekki'noono language [5,[11][12][13][14][15]. All of the sources which have been explored are to support as input for the Shekki'noono language tagger.…”

Section: Related Workmentioning

confidence: 99%

“…Siraj [11] developed the SomaliPart of Speech Tagger using machine learning Approach. The researcher developed a POS tagger using different machine learning approaches (i.e., HMM and CRF) and neural network model.…”

Section: Related Workmentioning

confidence: 99%

“…Also, the Somali POS tagger [11] was proposed using HMM, CRF, and neural networks for part-of-speech tagging for Somali language. The highest accuracies have been registered by all three HMM, CRF, and neural networks.…”

Section: Comparision With Previous Workmentioning

confidence: 99%

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A Hidden Markov Model-based Part of Speech Tagger for Shekki’noono Language

Chiche¹,

Kadi²,

Bekele³

2021

IJC

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Natural language processing plays a great role in providing an interface for human-computer communication. It enables people to talk with the computer in their formal language rather than machine language. This study aims at presenting a Part of speech tagger that can assign word class to words in a given paragraph sentence. Some of the researchers developed parts of speech taggers for different languages such as English Amharic, Afan Oromo, Tigrigna, etc. On the other hand, many other languages do not have POS taggers like Shekki’noono language. POS tagger is incorporated in most natural language processing tools like machine translation, information extraction as a basic component. So, it is compulsory to develop a part of speech tagger for languages then it is possible to work with an advanced natural language application. Because those applications enhance machine to machine, machine to human, and human to human communications. Although, one language POS tagger cannot be directly applied for other languages POS tagger. With the purpose for developing the Shekki’noono POS tagger, we have used the stochastic Hidden Markov Model. For the study, we have used 1500 sentences collected from different sources such as newspapers (which includes social, economic, and political aspects), modules, textbooks, Radio Programs, and bulletins. The collected sentences are labeled by language experts with their appropriate parts of speech for each word. With the experiments carried out, the part of speech tagger is trained on the training sets using Hidden Markov model. As experiments showed, HMM based POS tagging has achieved 92.77 % accuracy for Shekki’noono. And the POS tagger model is compared with the previous experiments in related works using HMM. As a future work, the proposed approaches can be utilized to perform an evaluation on a larger corpus.

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Section: B Algorithm For Implementing Hmmsupporting

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparision With Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

A Hidden Markov Model-based Part of Speech Tagger for Shekki’noono Language

Chiche¹,

Kadi²,

Bekele³

2021

IJC

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“…The current study extends these recent acoustic inquiries to examining applications of machine learning algorithms to describing the acoustic characteristics of allophonic systems operating within an under-resourced, endangered language spoken in Central America. Although machine learning technologies have been applied to data from endangered languages primarily for purposes of automatic speech recognition in the past (Besacier, Barnard, Karpov, and Schultz 2014;Rey and Nagy 2018;Mohammed 2020), the current study aims to use a machine learning algorithm to generate clusters of acoustic similarity for vocalic productions to explore patterns of phonologically-conditioned allophonic splits in Tol.…”

mentioning

confidence: 99%

Automatic phonetic classification of vocalic allophones in Tol

Bissell

2021

Proc Ling Soc Amer

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The aim of the present study involving automatic phonetic classification of /e/ and /u/ tokens in Tol is two-fold: first, I test existing claims about allophonic variation within these vowel classes, and second, I investigate allophonic variation within these vowel classes that has yet to be documented. The acoustic phonetic classifications derived in the present study contribute to a more detailed understanding of the allophonic systems operating within the Tol language. Operationalizing machine learning algorithms to investigate under-resourced, indigenous languages has the potential to provide detailed insights into the acoustic phonetic dynamics of a diverse range of vocalic systems.

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