NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets
Year: 2018
Type: conference-paper
Abstract: This paper describes our participation in Se-mEval 2018 Task 3 on Irony Detection in Tweets.We combine linguistic features with pre-trained activations of a neural network.The CNN is trained on the emoji prediction task.We combine the two feature sets and feed them into an XGBoost Classifier for classification.Subtask-A involves classification of tweets into ironic and non-ironic instances, whereas Subtask-B involves classification of tweets into non-ironic, verbal irony, situational irony or other verbal irony.It is observed that combining features from these two different feature spaces improves our system results.We leverage the SMOTE algorithm to handle the problem of class imbalance in Subtask-B.Our final model achieves an F1-score of 0.65 and 0.47 on Subtask-A and Subtask-B respectively.Our system ranks 4 th on both tasks, respectively, outperforming the baseline by 6% on Subtask-A and 14% on Subtask-B.
DOI: 10.18653/v1/s18-1104
Institution: Indian Institute of Technology BHU
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2805535038