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Found 8 papers, 0 papers with code
ssn_diBERTsity@LT-EDI-EACL2021:Hope Speech Detection on multilingual YouTube comments via transformer based approach
In recent times, there exists an abundance of research to classify abusive and offensive texts focusing on negative comments but only minimal research using the positive reinforcement approach.
Ssn\_nlp at SemEval 2020 Task 12: Offense Target Identification in Social Media Using Traditional and Deep Machine Learning Approaches
Offensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that is addressed towards an individual or a group.
SSN\_NLP\_MLRG at SemEval-2020 Task 12: Offensive Language Identification in English, Danish, Greek Using BERT and Machine Learning Approach
Offensive language identification is to detect the hurtful tweets, derogatory comments, swear words on social media.
SSN-NLP at SemEval-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks
Common sense validation deals with testing whether a system can differentiate natural language statements that make sense from those that do not make sense.
SSN\_NLP at SemEval-2020 Task 7: Detecting Funniness Level Using Traditional Learning with Sentence Embeddings
For subtask 2, Universal sentence encoder classifier achieves the highest accuracy for development set and Multi-Layer Perceptron applied on vectors vectorized using universal sentence encoder embeddings for the test set.
Sarcasm Identification and Detection in Conversion Context using BERT
As an immense growth of social media, sarcasm analysis helps to avoid insult, hurts and humour to affect someone.
SSN\_NLP at SemEval-2019 Task 3: Contextual Emotion Identification from Textual Conversation using Seq2Seq Deep Neural Network
We have evaluated our approach on the EmoContext@SemEval2019 dataset and we have obtained the micro-averaged F1 scores as 0. 595 and 0. 6568 for the pre-evaluation dataset and final evaluation test set respectively.
SSN\_NLP at SemEval-2019 Task 6: Offensive Language Identification in Social Media using Traditional and Deep Machine Learning Approaches
Offensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that degrades an individual or a group.
