Машиналық оқыту тәсілдері

method ID: machinelearningapproaches

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Authors: nan

Publication: TIPSTER (2013)

DOI: nan

This paper describes the NILC USP system that participated in SemEval-2013 Task 2: Sentiment Analysis in Twitter. Our system adopts a hybrid classification process that uses three classification approaches: rule- …...

22987

COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

This paper reports our submission to the Aspect-Based Sentiment Analysis task of Se- mEval 2016. It covers the prediction of sen- timent for a given set of aspects (e.g., sub- …...

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Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models

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Publication: TIPSTER (2020)

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AbstractThis paper presents the study of sentiment analysis for Amharic social media texts. As the number of social media users is ever-increasing, social media platforms would like to understand the …...

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FII UAIC at SemEval-2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and Arabic

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AbstractThe “iSarcasmEval - Intended Sarcasm Detection in English and Arabic” task at the SemEval 2022 competition focuses on detectingand rating the distinction between intendedand perceived sarcasm in the context of …...

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Clark Kent at SemEval-2023 Task 5: SVMs, Transformers, and Pixels for Clickbait Spoiling

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Publication: TIPSTER (2023)

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AbstractIn this paper we present an analysis of our approaches for the 2023 SemEval-2023 Clickbait Challenge. We only participated in the sub-task aiming at identifying different clikcbait spoiling types comparing …...

24804