ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods
Zhenghang Yin | Feixiang Wang | Man Lan | Wenting Wang
Proceedings of the 12th International Workshop on Semantic Evaluation
The paper describes our submissions to task 3 in SemEval-2018. There are two subtasks: Subtask A is a binary classification task to determine whether a tweet is ironic, and Subtask B is a fine-grained classification task including four classes. To address them, we explored supervised machine learning method alone and in combination with neural networks.
ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models
Feixiang Wang | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
This paper describes our submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours. Given a rumoured tweet and a lot of reply tweets, the subtask A is to label whether these tweets are support, deny, query or comment, and the subtask B aims to predict the veracity (i.e., true, false, and unverified) with a confidence (in range of 0-1) of the given rumoured tweet. For both subtasks, we adopted supervised machine learning methods, incorporating rich features. Since training data is imbalanced, we specifically designed a two-step classifier to address subtask A .
ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking
Feixiang Wang | Zhihua Zhang | Man Lan
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)