Part of Speech Tagging of Marathi Text Using Trigram Method

Singh, Jyoti; Joshi, Nisheeth; Mathur, Iti

doi:10.5121/ijait.2013.3203

Cited by 22 publications

(10 citation statements)

References 4 publications

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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%

“…It is one of the useful tasks in E Natural Language Processing (NLP). It plays an important role in Speech and NLP such as Speech Recognition, Speech Synthesis, Information Retrieval, word sense disambiguation, and machine translation [5,6]. Parts of speech ambiguity is a common feature of a majority of the world's languages.…”

Section: Introductionmentioning

confidence: 99%

“…It is a process of assigning a tag to every word in a sentence that serves as a preliminary task for carrying out tasks like chunking, dependency parsing, and named-entity recognition on any language. All of these NLP systems must use part of speech tagger as their preprocessor components for their best performance [6,7].…”

Section: Introductionmentioning

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: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Chiche¹,

Kadi²,

Bekele³

2021

IJC

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“…These models are experimented with (HMM-S+IMA and HMM-SS+IMA) show complete morphological restriction and (HMM-S-CMA and HMM-SS-CMA) show complete morphological restriction. Singh [8] POS tagger for Marathi language using Trigram model Corpus consists of 2000 sentences i.e. 48,635 words Accuracy gained is 91.63%.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Part of Speech Tagger Using Deep Learning

Singh*¹,

Verma²,

Seal³

et al. 2019

IJEAT

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Part of speech tagging is the initial step in development of NLP (natural language processing) application. POS Tagging is sequence labelling task in which we assign Part-of-speech to every word (Wi) which is sequence in sentence and tag (Ti) to corresponding word as label such as (Wi/Ti…. Wn/Tn). In this research project part of speech tagging is perform on Hindi. Hindi is the fourth most popular language and spoken by approximately 4billion people across the globe. Hindi is free word-order language and morphologically rich language due to this applying Part of Speech tagging is very challenging task. In this paper we have shown the development of POS tagging using neural approach.

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“…They used their own 32 tags tagset for annotating the corpus and reported an accuracy of 95.64%. For Marathi, Singh et al [15] proposed a POS tagger using trigram method. They used a pos tagset proposed by Bharti et al [16] which had 24 tags.…”

Section: Literature Surveymentioning

confidence: 99%