Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection
نویسندگان
چکیده
In this paper, we compare feature-based and Neural Network-based approaches on the supervised stance classification task for tweets in SemEval-2016 Task 6 Subtask A (Mohammad et al., 2016). In the feature-based approach, we use external resources such as lexicons and crawled texts. The Neural Network based approach employs Convolutional Neural Network (CNN). Our results show that the feature-based model outperformed the CNN model on the test data although the CNN model was better than the feature-based model in the cross validation on the training data.
منابع مشابه
pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection
In this paper, we develop a convolutional neural network for stance detection in tweets. According to the official results, our system ranks 1 on subtask B (among 9 teams) and ranks 2 on subtask A (among 19 teams) on the twitter test set of SemEval2016 Task 6. The main contribution of our work is as follows. We design a ”vote scheme” for prediction instead of predicting when the accuracy of val...
متن کاملIKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification
This paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural network for short text categorization using multiple filter sizes. Our approach beats the baseline classifiers on different event data with good F1 scores. The best of our submitted runs achieves rank 1 st among al...
متن کاملDeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
This paper describes our approach for the Detecting Stance in Tweets task (SemEval-2016 Task 6). We utilized recent advances in short text categorization using deep learning to create word-level and character-level models. The choice between word-level and characterlevel models in each particular case was informed through validation performance. Our final system is a combination of classifiers ...
متن کاملAn efficient method for cloud detection based on the feature-level fusion of Landsat-8 OLI spectral bands in deep convolutional neural network
Cloud segmentation is a critical pre-processing step for any multi-spectral satellite image application. In particular, disaster-related applications e.g., flood monitoring or rapid damage mapping, which are highly time and data-critical, require methods that produce accurate cloud masks in a short time while being able to adapt to large variations in the target domain (induced by atmospheric c...
متن کاملMITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
We describe MITRE’s submission to the SemEval-2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, producing an average F1 score of 67.8 when assessing whether a tweet author was in favor or against a topic. We employed a recurrent neural network initialized with features learned via distant supervision on two large unlabeled data...
متن کامل