ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets
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Feature EngineeringSentiment AnalysisStance DetectionSimilar Papers 제목 키워드 기반
ECNU at SemEval-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in Reviews
2016-06-01 · SEMEVAL 2016 6
· Mengxiao Jiang, Zhihua Zhang, Man Lan
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)SentenceSentiment Analysis
ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation
2020-07-28 · SEMEVAL 2020
· Qian Zhao, Siyu Tao, Jie zhou, LinLin Wang 외
This paper describes our system for SemEval-2020 Task 4: Commonsense Validation and Explanation (Wang et al., 2020). We propose a novel Knowledge-enhanced Graph Attention Network (KEGAT) architecture for this task, lever…
Data AugmentationGraph AttentionECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking
2016-06-01 · SEMEVAL 2016 6
· Feixiang Wang, Zhihua Zhang, Man Lan
Learning-To-RankSentiment Analysis
ECNUCS: A Surface Information Based System Description of Sentiment Analysis in Twitter in the SemEval-2013 (Task 2)
2013-06-01 · SEMEVAL 2013 6
· Tiantian Zhu, Fangxi Zhang, Lan Man
Sentiment AnalysisTask 2
ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models
2017-08-01 · SEMEVAL 2017 8
· Feixiang Wang, Man Lan, Yuanbin Wu
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 …
BIG-bench Machine LearningRumour DetectionSentiment Analysis