SemEval-2016 Task 6: Detecting Stance in Tweets
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Tasks
Information RetrievalNatural Language InferenceStance DetectionText SummarizationSimilar Papers 제목 키워드 기반
JU\_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines
2016-06-01 · SEMEVAL 2016 6
· Braja Gopal Patra, Dipankar Das, B, Sivaji yopadhyay
Information RetrievalNatural Language InferenceOpinion MiningSentiment Analysis+2
NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets
2016-06-01 · SEMEVAL 2016 6
· Amita Misra, Brian Ecker, H, Theodore leman 외
Sentiment Analysis
IDI@NTNU at SemEval-2016 Task 6: Detecting Stance in Tweets Using Shallow Features and GloVe Vectors for Word Representation
2016-06-01 · SEMEVAL 2016 6
· Henrik B{\o}hler, Petter Asla, Erwin Marsi, Rune S{\ae}tre
Sentiment AnalysisStance Detection
ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets
2016-06-01 · SEMEVAL 2016 6
· Zhihua Zhang, Man Lan
Feature EngineeringSentiment AnalysisStance Detection
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
2016-06-17 · SEMEVAL 2016 6
· Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, Deb Roy
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 model…
Data AugmentationText Categorization