Fernando, S; Ranathunga, S

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dc.contributor.author	Fernando, S
dc.contributor.author	Ranathunga, S
dc.contributor.editor	Chathuranga, D
dc.date.accessioned	2022-09-01T09:37:38Z
dc.date.available	2022-09-01T09:37:38Z
dc.date.issued	2018-05
dc.identifier.citation	S. Fernando and S. Ranathunga, "Evaluation of Different Classifiers for Sinhala POS Tagging," 2018 Moratuwa Engineering Research Conference (MERCon), 2018, pp. 96-101, doi: 10.1109/MERCon.2018.8421997.	en_US
dc.identifier.uri	http://dl.lib.uom.lk/handle/123/18833
dc.description.abstract	This paper presents a comparative evaluation of three state-of-the-art classifiers for Sinhala Parts-of-Speech (POS) tagging. Support Vector Machines (SVM), Hidden Markov Models (HMM) and Conditional Random Fields (CRF) based POS tagger models are generated and tested using different combinations of a corpus of news articles and a corpus of official government documents. CRF is used for the first time in Sinhala POS tagging, thus the best feature set is experimentally derived. To further improve the accuracy of POS tagging, a majority voting based ensemble tagger is created using three individual taggers. This ensemble tagger achieved the highest accuracy in POS tagging than any individual tagger. The two domains (news, and official government documents) used in this study have noticeable differences in writing style and vocabulary. Generating domain specific POS taggers is time consuming and costly due to the overhead involved in creating and manually tagging domain specific corpora, for low resourced languages in particular. Therefore, this study also evaluates the possibility and successfulness of using corpora of different domains in training and testing phases of aforementioned machine learning techniques.	en_US
dc.language.iso	en	en_US
dc.publisher	IEEE	en_US
dc.relation.uri	https://ieeexplore.ieee.org/document/8421997	en_US
dc.subject	Sinhala	en_US
dc.subject	Parts-of-Speech (POS)	en_US
dc.subject	HMM	en_US
dc.subject	CRF	en_US
dc.subject	Ensemble	en_US
dc.subject	Ensemble	en_US
dc.title	Evaluation of different classifiers for sinhala pos tagging	en_US
dc.type	Conference-Full-text	en_US
dc.identifier.faculty	Engineering
dc.identifier.department	Engineering Research Unit, University of Moratuwa	en_US
dc.identifier.year	2018	en_US
dc.identifier.conference	2018 Moratuwa Engineering Research Conference (MERCon)	en_US
dc.identifier.place	Moratuwa, Sri Lanka	en_US
dc.identifier.pgnos	pp. 96-101	en_US
dc.identifier.proceeding	Proceedings of 2018 Moratuwa Engineering Research Conference (MERCon)	en_US
dc.identifier.doi	10.1109/MERCon.2018.8421997	en_US