![]() Ambo, Shekki’noone Fiinniiyeessona shicheesse sheero, Maasha, Shekka: unpublished, version 3, 2017. ![]() 69–74, 2018.ĭiesn, “Part of speech tagging for English text data,” School of Computer Science, Carnegie Mellon University, Unpublished, p. Shashirekha, “Machine learning approaches for amharic parts-of-speech tagging,” Proceedings of the ICON-2018, Patiala, India, pp. ![]() Dias, “Hidden Markov model based on art of speech tagger for Sinhala language,” International Journal on Natural Language Computing (IJNLC), vol. Ashuboda, “Tigrinya part-of-speech tagging with morphological patterns and the new Nagaoka Tigrinya corpus,” Int J Comput Appl, vol. thesis, Addis Ababa University, School of Graduate Studies, College of Natural Sciences, Department of Computer Science, November, 2013. Mekuria, Design and Development of Part-of-Speech Tagger for Kafi-Noonoo Language, MSc. Tachbelie, “Designing and creation of pronunciation lexicons for speech processing in under-resourced and morphologically rich languages: The case of Amharic,” Project Report of a Google supported research, 2014. Gamback, “Tagging and verifying an Amharic news corpus,” Proceedings of the Workshop on Language Technology for Normalisation of Less-Resourced Languages (SALTMIL8/AfLaT2012), 2012, pp. Singha, “Part of speech tagging in Manipuri with hidden Markov model,” IJCSI International Journal of Computer Science, vol. Mohammed, “Using machine learning to build POS tagger for under-resourced language: the case of Somali,” International Journal Information Technology, vol. Thesis, Addis Ababa University, Addis Ababa, 2013.Ī. Fantahun, Unsupervised Part of Speech Tagger for Amharic Language, MSc. Leonel, “A proposal of a morphological tagger for Spanish based on Cuban corpora,” Proceedings of the International Conference on Recent Advances in Natural Language Processing, Borovets, Bulgaria, pp. Kumar, “Part of speech tagger for morphologically rich Indian languages: A survey,” International Journal of Computer Applications, vol. Mathur, “Part of speech tagging of Marathi text abstract using trigram method,” International Journal of Advanced Information Technology, vol. Getnet, Unsupervised POS tagging for Amharic, Master's Thesis, University of Gondar, Ethiopia, unpublished, 2015. Million, “Parts of speech tagging for Afaan Oromo,” Int J Adv Comput Sci Appl, 2015. Muhammad, “Parts-of-speech tagging of Hausa-based texts using hidden Markov model,” Dutse Journal of Pure and Applied Sciences (DUJOPAS), vol. Vinesh, “POS tagging approaches: A comparison,” International Journal of Computer Applications, vol. Fethijarra, “Genetic approach tagging,” International Journal on Natural Language Computing (IJNLC), vol. As a future work, the proposed approaches can be utilized to perform an evaluation on a larger corpus.ī. And the POS tagger model is compared with the previous experiments in related works using HMM. As experiments showed, HMM based POS tagging has achieved 92.77 % accuracy for Shekki’noono. With the experiments carried out, the part of speech tagger is trained on the training sets using Hidden Markov model. The collected sentences are labeled by language experts with their appropriate parts of speech for each word. 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. With the purpose for developing the Shekki’noono POS tagger, we have used the stochastic Hidden Markov Model. Although, one language POS tagger cannot be directly applied for other languages POS tagger. ![]() Because those applications enhance machine to machine, machine to human, and human to human communications. 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. POS tagger is incorporated in most natural language processing tools like machine translation, information extraction as a basic component. On the other hand, many other languages do not have POS taggers like Shekki’noono language. Some of the researchers developed parts of speech taggers for different languages such as English Amharic, Afan Oromo, Tigrigna, etc. This study aims at presenting a Part of speech tagger that can assign word class to words in a given paragraph sentence. It enables people to talk with the computer in their formal language rather than machine language. Natural language processing plays a great role in providing an interface for human-computer communication. Parts of speech tagger, HMM, NLP, Shekki’noono language, Bigram Abstract
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