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Turing at SemEval-2017 Task 8: Sequential Approach to Rumour StanceClassif i cation with Branch-LSTMElena Kochkina 12 , Maria Liakata 12 , Isabelle Augenstein 31University of Warwick, Coventry, United Kingdom2Alan Turing Institute, London, United Kingdom3University College London, London, United...
Vechtomova, UWaterloo at SemEval-2017 Task 8: Detect- ing Stance towards Rumours with Topic Independent Features, in: Proceed- ings of SemEval, ACL, 2017, pp. 461-464.Hareesh Bahuleyan and Olga Vechtomova. 2017. Uwaterloo at semeval-2017 task 8: Detecting stance towards rumours with ...
SemEval-2013 Task 2: Sentiment Analysis in Twitter In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, complicating the com... P...
SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity This paper describes the SimiHawk system submission from UMass Lowell for the core Semantic Textual Similarity task at SemEval-2016. We built four systems: a small feature-based system that leverages word ali...
semeval-2016_2017-task3-subtaskB-english.json.gz(6.05M) – Example: {"2016-dev": [ {"ORGQ_ID":"Q268","OrgQBody":"Which is a good bank as per your experience in Doha","OrgQSubject":"Good Bank","Threads": [ {"THREAD_SEQUENCE":"Q268_R4","RelQuestion": {"RELQ_CATEGORY":...
we only focus on task A since it tends to be the most popular one. Moreover, in order to be consistent with historical editions of this competition, we use the averageF1subscript𝐹1F_{1}score of the positive and negative class as the metric of interest. This is different from the macr...
R. El-Beltagy, NileTMRG at SemEval-2017 Task 8: Determin- ing Rumour and Veracity Support for Rumours on Twitter, in: Proceedings of SemEval, ACL, 2017, pp. 470-474.O. Enayet and S. R. El-Beltagy, "NileTMRG at SemEval-2017 Task 8: Determining rumour and veracity support for ...
We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios...
We evaluated this system on the SemEval-2017 English sentiment analysis task. In terms of average F1-Score, our system obtained 8~(th) position out of 39 submissions (F1-Score: 0.632, average recall: 0.637, accuracy: 0.646).Abeed Sarker...