Papers by Manos Papagelis
Learning Emotion-enriched Word Representations (C18-1)
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| Challenge: | Existing word representations based on distributional hypothesis do not provide accurate representations of emotions. |
| Approach: | They propose a method to obtain emotion-enriched word representations by remote supervision using a large training dataset of text documents and two recurrent neural network architectures. |
| Outcome: | The proposed method outperforms competing general-purpose and affective representations on two tasks. |
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks (2020.acl-main)
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| Challenge: | Affective tasks such as sentiment analysis, emotion classification and sarcasm detection have enjoyed great popularity in recent years. |
| Approach: | They conduct a comprehensive analysis of the role of preprocessing techniques in affective analysis based on word vector models. |
| Outcome: | The proposed model is the first of its kind and provides useful insights on the role of each preprocessing technique when applied at the training phase, commonly ignored in pretrained word vector models, and/or at the downstream task phase. |
Affective and Contextual Embedding for Sarcasm Detection (2020.coling-main)
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| Challenge: | Existing methods to detect sarcasm from text lack vocal intonation or facial gestures in textual data. |
| Approach: | They propose two deep neural network models for sarcasm detection that extend the architecture of BERT by incorporating both affective and contextual features. |
| Outcome: | The proposed models outperform state-of-the-art models on different datasets with significant margins. |