Search Results for author: Avi Arampatzis

Found 6 papers, 1 papers with code

DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021?

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.

Classification regression

DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification

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.

BIG-bench Machine Learning feature selection +1

DUTH at SemEval-2019 Task 8: Part-Of-Speech Features for Question Classification

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.

Community Question Answering Fact Checking +3

DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets

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.

Information Retrieval Task 2

DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis

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.

BIG-bench Machine Learning General Classification +3

DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles

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.

BIG-bench Machine Learning Feature Engineering +3

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