Bridging the digital divide in Sri Lanka: some challenges and opportunities in using Sinhala in ICT

Nandasara, S. T.; Mikami, Yoshiki

doi:10.4038/icter.v8i1.7162

Cited by 4 publications

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“…Given that it was a decade old by the time the first edition of this survey was compiled, we observe the existence of many new discoveries in Sinhala NLP which have not been taken into account by it. A review on some challenges and opportunities of using Sinhala in computer science was done by Nandasara and Mikami [56]. At this point, it is worthy to note that the largest number of studies in Sinhala NLP has been on optical character recognition (OCR) rather than on higher levels of the hierarchy shown in Figure 1.…”

Section: Sinhala Nlp Resourcesmentioning

confidence: 99%

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Silva¹

2019

Preprint

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Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo-European. However, due to poverty in both linguistic and economic capital, Sinhala, in the perspective of Natural Language Processing tools and research, remains a resource-poor language which has neither the economic drive its cousin English has nor the sheer push of the law of numbers a language such as Chinese has. A number of research groups from Sri Lanka have noticed this dearth and the resultant dire need for proper tools and research for Sinhala natural language processing. However, due to various reasons, these attempts seem to lack coordination and awareness of each other. The objective of this paper is to fill that gap of a comprehensive literature survey of the publicly available Sinhala natural language tools and research so that the researchers working in this field can better utilize contributions of their peers. As such, we shall be uploading this paper to arXiv and perpetually update it periodically to reflect the advances made in the field.

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Section: Sinhala Nlp Resourcesmentioning

confidence: 99%

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Silva¹

2019

Preprint

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“…The Sinhala language has a remarkable history and it belongs to the Indo-Aryan language family which is a subfamily of Indo-European [1]. It consists of 18 vowels and 42 consonants, but now only uses 16 vowels and 41 consonants [17]. Since Sinhala is a morphologically rich language [1], it is a very complex task to make algorithmic natural language processing resources.…”

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confidence: 99%

Identification of Abusive Sinhala Comments in Social Media using Text Mining and Machine Learning Techniques

Sandaruwan

Lorensuhewa

Kalyani

2020

Int J on Adv. in ICT for Emerging Countries

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With the technology revolution, most of the natural languages that are used all over the world have won the digital world. Therefore, people use modern technologies such as Social Media and the Internet with their native languages. As a result, people who are with self-ego on their tradition, race, caste, religion and other social factors, tend to make abusiveness on others who do not belong to the same social group by their native languages. Since the Social Media platforms do not have centralized control, it has become a good platform to advertise their backward ideas without being governed and monitored. The Sinhala language has also been added to most famous Social Media platforms. Though the Sinhala language has more than 2500 years of history, it does not have rich resources for computer-based natural language processing. Therefore, it has been a very difficult task to automatically detect Sinhala abusive comments which are being published and shared among Social Media platforms. Therefore, here, we have used evenly distributed 2000 Sinhala comment corpus among offensive and neutral classes to train three different models: Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM) and, Random Forest Decision Tree (RFDT) and the features were extracted from Bag of Word model, word n-gram model, character n-gram model, and word skip-gram model to automatically detect Sinhala abusive comments. After the training process, each model was tested with 200 evenly distributed comment corpus and MNB showed the highest accuracy of 96.5% with 96% average recall for both character tri-gram and character four-gram models. Further, two lexiconbased approaches called cross-lingual lexicon approach and corpus-based lexicon approach were considered to detect Sinhala abusive comments. From these two lexicon based approaches, the corpus-based lexicon gave the highest accuracy of 90.5% with an average recall of 90.5%.

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A Hybrid Decision Tree for Printed Sinhala Character Recognition Using SVMs

Anuraj

Charles

2018

2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS)

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Bridging the digital divide in Sri Lanka: some challenges and opportunities in using Sinhala in ICT

Cited by 4 publications

References 0 publications

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Identification of Abusive Sinhala Comments in Social Media using Text Mining and Machine Learning Techniques

A Hybrid Decision Tree for Printed Sinhala Character Recognition Using SVMs

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