Analysis of implemented part of speech tagger approaches: The case of Ethiopian languages

Demilie, Wubetu Barud

doi:10.17485/ijst/v13i48.1876

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…Consequently, automated PoS tagging is a method for automatically annotating lexical categories. The procedure accepts a word or a sentence as input, assigns it with the correct tag, and produces the tagged text (Mohammed, 2020) and (Demilie, 2020). PoS tagging was first investigated during the sixties by (Harris, 1962) and (Klein & Simmons, 1963) using hand-written rules.…”

Section: Related Workmentioning

confidence: 99%

An Hybrid Part of Speech Tagger for Setswana Language using a Voting Method

Dibitso¹,

Owolawi²,

Ojo³

2022

ICONIC

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Part-of Speech (PoS) tagging is a corpus linguistics that deals with assigning appropriate lexical categories to each word in a sentence. To effectively address challenges associated with PoS tagging, several Natural Language Processing (NLP) tasks modelling techniques have been employed, including Conditional Random Fields (CRF), Support Vector Machines (SVM), and Decision Trees in diverse languages. These PoS taggers implement the process of associating the correct PoS (nouns, verbs, adjectives, adverbs, etc.) with each word in a sentence. However, creating language resources is an expensive process for many languages, including the indigenous languages of South Africa that are classified as resource-scarce. Therefore, using Setswana as a language with limited resources, this study explores and applies methods to increase the utilization of existing resources and tagger accuracy. This is done using Setswana's two PoS taggers: a Maximum Entropy (MaxEnt) and an SVM, which achieved an accurateness of 94.4 per cent and 95.59 per cent respectively. To find errors in the taggers, an error analysis is carried out. The Setswana PoS Tagger was then built using a voting algorithm to improve results and attain 97.06 per cent accuracy. The combination of taggers reduces the error rate by up to 2.01 per cent.

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Section: Related Workmentioning

confidence: 99%

An Hybrid Part of Speech Tagger for Setswana Language using a Voting Method

Dibitso¹,

Owolawi²,

Ojo³

2022

ICONIC

View full text Add to dashboard Cite

show abstract

“…improves human-to-human communication, enables human-to-machine communication by doing useful processing of texts or speeches. Part-of-speech (POS) tagging is one of the most important addressed areas and main building block and application in the natural language processing discipline [1][2][3]. So, Part of Speech (POS) Tagging is a notable NLP topic that aims in assigning each word of a text the proper syntactic tag in its context of appearance [4][5][6][7][8].…”

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confidence: 99%

Part of speech tagging: a systematic review of deep learning and machine learning approaches

2022

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Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.

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State of the Art, Recent Developments, and Future Directions in Applying Deep Learning to Part of Speech Tagging in NLP

Kaushal

Chadha

2022

2022 International Conference on Computational Modelling, Simulation and Optimization (ICCMSO)

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Analysis of implemented part of speech tagger approaches: The case of Ethiopian languages

Cited by 9 publications

References 7 publications

An Hybrid Part of Speech Tagger for Setswana Language using a Voting Method

An Hybrid Part of Speech Tagger for Setswana Language using a Voting Method

Part of speech tagging: a systematic review of deep learning and machine learning approaches

State of the Art, Recent Developments, and Future Directions in Applying Deep Learning to Part of Speech Tagging in NLP

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