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Is Meta Embedding better than pre-trained word embedding to perform Sentiment Analysis for Dravidian Languages in Code-Mixed Text?

dc.contributor.authorChanda S.; Singh R.P.; Pal S.
dc.date.accessioned2025-05-23T11:27:28Z
dc.description.abstractThis paper describes the IRlab@IITBHU system for the Dravidian-CodeMix - FIRE 2021: Sentiment Analysis for Dravidian Languages pairs Tamil-English (TA-EN), Kannada-English (KN-EN), and Malayalam-English (ML-EN) in Code-Mixed text. We have reported three models output in this paper where We have submitted only one model for sentiment analysis of all code-mixed datasets. Run-1 was obtained from the FastText embedding with multi-head attention, Run-2 used the meta embedding techniques, and Run-3 used the Multilingual BERT(mBERT) model for producing the results. Run-2 outperformed Run-1 and Run-3 for all the language pairs. © 2021 Copyright for this paper by its authors.
dc.identifier.doiDOI not available
dc.identifier.urihttp://172.23.0.11:4000/handle/123456789/11444
dc.relation.ispartofseriesCEUR Workshop Proceedings
dc.titleIs Meta Embedding better than pre-trained word embedding to perform Sentiment Analysis for Dravidian Languages in Code-Mixed Text?

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