Hidden Markov Model Based Part of Speech Tagger for Sinhala Language

Jayaweera, A.J.P.M.P.; Dias, N.G.J.

doi:10.5121/ijnlc.2014.3302

Cited by 18 publications

(11 citation statements)

References 2 publications

(3 reference statements)

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“…The application of the methods in Hidden Markov Model based part of speech tagging approach (which we have previously developed [1]) was evaluated and results were presented. The implementation was tested against 90551 words, 2754 sentences of Sinhala text corpus and that showed 91.5% accuracy in the tagging process with predicting tags to unknown words.…”

Section: Discussionmentioning

confidence: 99%

Unknown Words Analysis in POS Tagging of Sinhala Language

A.J.P.M.P¹,

N.G.J²

2014

MLAIJ

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Section: Discussionmentioning

confidence: 99%

Unknown Words Analysis in POS Tagging of Sinhala Language

A.J.P.M.P¹,

N.G.J²

2014

MLAIJ

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“…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].…”

Section: Related Workmentioning

confidence: 99%

“…Based on these the tagger scored 82.26 % accuracy and 66.73 % accuracy using statistical data and without statistical data respectively. The other author called Lanka and Science [17], used the same method, the Hidden Markov Model approach, to develop a part of speech tagger for the Sinhala language. Similarly, the researchers used 2754 sentences with 26 tagsets to develop the Sinhala language part of speech tagger.…”

Section: Related 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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“…So, to have high precision tagger is one of the importance tasks for NLP applications. Handling ambiguous and unknown words are the challenge of POS tagging [1,2]. For every NLP application such as machine translation, information extraction, speech recognition, grammar checking and word sense disambiguation, etc are needed to do word segmentation and Part-of-speech (POS) tagging of a fundamental process of natural language processing application.…”

Section: Introductionmentioning

confidence: 99%

Improving accuracy of Part-of-Speech (POS) tagging using hidden markov model and morphological analysis for Myanmar Language

Cing¹,

Soe²

2020

IJECE

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In Natural Language Processing (NLP), Word segmentation and Part-of-Speech (POS) tagging are fundamental tasks. The POS information is also necessary in NLP’s preprocessing work applications such as machine translation (MT), information retrieval (IR), etc. Currently, there are many research efforts in word segmentation and POS tagging developed separately with different methods to get high performance and accuracy. For Myanmar Language, there are also separate word segmentors and POS taggers based on statistical approaches such as Neural Network (NN) and Hidden Markov Models (HMMs). But, as the Myanmar language's complex morphological structure, the OOV problem still exists. To keep away from error and improve segmentation by utilizing POS data, segmentation and labeling should be possible at the same time.The main goal of developing POS tagger for any Language is to improve accuracy of tagging and remove ambiguity in sentences due to language structure. This paper focuses on developing word segmentation and Part-of- Speech (POS) Tagger for Myanmar Language. This paper presented the comparison of separate word segmentation and POS tagging with joint word segmentation and POS tagging.

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Hidden Markov Model Based Part of Speech Tagger for Sinhala Language

Abstract: In this paper we present a fundamental lexical semantics of Sinhala language and a Hidden Markov Model (HMM)

Cited by 18 publications

References 2 publications

Unknown Words Analysis in POS Tagging of Sinhala Language

Unknown Words Analysis in POS Tagging of Sinhala Language

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

Improving accuracy of Part-of-Speech (POS) tagging using hidden markov model and morphological analysis for Myanmar Language

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