This paper presents the participation of the IRNLPDAIICT team from Information Retrieval and Natural Language Processing lab at DA-IICT, India in DravidianLangTech-EACL2021 Offensive Language identification in Dravidian Languages. The aim of this shared task is to identify Offensive Language from a code-mixed data-set of YouTube comments. The task is to classify comments into Not Offensive (NO), Offensive Untargetede(OU), Offensive Targeted Individual (OTI), Offensive Targeted Group (OTG), Offensive Targeted Others (OTO), Other Language (OL) for three Dravidian languages: Kannada, Malayalam and Tamil. We use TF-IDF character n-grams and pretrained MuRIL embeddings for text representation and Logistic Regression and Linear SVM for classification. Our best approach achieved Ninth, Third and Eighth with weighted F1 score of 0.64, 0.95 and 0.71in Kannada, Malayalam and Tamil on test dataset respectively.

This paper presents the participation of the IRNLP_DAIICT team from Information Retrieval and Natural Language Processing lab at DA-IICT, India in LT-EDI@EACL2021 Hope Speech Detection task. The aim of this shared task is to identify hope speech from a code-mixed data-set of YouTube comments. The task is to classify comments into Hope Speech, Non Hope speech or Not in language, for three languages: English, Malayalam-English and Tamil-English. We use TF-IDF character n-grams and pretrained MuRIL embeddings for text representation and Logistic Regression and Linear SVM for classification. Our best approach achieved second, eighth and fifth rank with weighted F1 score of 0.92, 0.75 and 0.57 in English, Malayalam-English and Tamil-English on test dataset respectively