no code implementations • SEMEVAL 2021 • Alexandros Karasakalidis, Dimitrios Effrosynidis, Avi Arampatzis
This paper describes the approach that was developed for SemEval 2021 Task 7 (Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks) by the DUTH Team.
1 code implementation • SEMEVAL 2020 • Anastasios Bairaktaris, Symeon Symeonidis, Avi Arampatzis
This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles.
no code implementations • SEMEVAL 2019 • Anastasios Bairaktaris, Symeon Symeonidis, Avi Arampatzis
This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums.
no code implementations • SEMEVAL 2018 • Dimitrios Effrosynidis, Georgios Peikos, Symeon Symeonidis, Avi Arampatzis
This paper describes the approach that was developed for SemEval 2018 Task 2 (Multilingual Emoji Prediction) by the DUTH Team.
no code implementations • SEMEVAL 2017 • Symeon Symeonidis, Dimitrios Effrosynidis, John Kordonis, Avi Arampatzis
This report describes our participation to SemEval-2017 Task 4: Sentiment Analysis in Twitter, specifically in subtasks A, B, and C. The approach for text sentiment classification is based on a Majority Vote scheme and combined supervised machine learning methods with classical linguistic resources, including bag-of-words and sentiment lexicon features.
no code implementations • SEMEVAL 2017 • Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis, Avi Arampatzis
We present the system developed by the team DUTH for the participation in Semeval-2017 task 5 - Fine-Grained Sentiment Analysis on Financial Microblogs and News, in subtasks A and B.