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

Supriya Chanda, Rajat Pratap Singh, Sukomal Pal
December, 2021
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Abstract

This 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 MalayalamEnglish (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.

Type
Preprint
Publication
Forum for Information Retrieval Evaluation
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Supriya Chanda
Supriya Chanda
Research Scholar (2018-2024)

Supriya Chanda (pronounced as Supriyo), completed his Ph.D in the Department of Computer Science and Engineering, IIT (BHU), Varanasi. He did his research under the guidance of Dr. Sukomal Pal at the Information retrieval lab.