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        <title>Neural Feature Extraction for Contextual Emotion Detection</title>
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        <namePart type="given">Elham</namePart>
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    <name type="personal">
        <namePart type="given">Leila</namePart>
        <namePart type="family">Kosseim</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>This paper describes a new approach for the task of contextual emotion detection. The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a classifier, that can be neural or SVM-based. We evaluated the model with the dataset of the task 3 of SemEval 2019 (EmoContext), which includes short 3-turn conversations, tagged with 4 emotion classes. The best performing setup was achieved using ELMo word embeddings and POS tags as input, bidirectional GRU as hidden units, and an SVM as the final classifier. This configuration reached 69.93% in terms of micro-average F1 score on the main 3 emotion classes, a score that outperformed the baseline system by 11.25%.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_091</identifier>
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%0 Conference Proceedings
%T Neural Feature Extraction for Contextual Emotion Detection
%A Mohammadi, Elham
%A Amini, Hessam
%A Kosseim, Leila
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F mohammadi-etal-2019-neural
%X This paper describes a new approach for the task of contextual emotion detection. The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a classifier, that can be neural or SVM-based. We evaluated the model with the dataset of the task 3 of SemEval 2019 (EmoContext), which includes short 3-turn conversations, tagged with 4 emotion classes. The best performing setup was achieved using ELMo word embeddings and POS tags as input, bidirectional GRU as hidden units, and an SVM as the final classifier. This configuration reached 69.93% in terms of micro-average F1 score on the main 3 emotion classes, a score that outperformed the baseline system by 11.25%.
%R 10.26615/978-954-452-056-4_091
%U https://aclanthology.org/R19-1091
%U https://doi.org/10.26615/978-954-452-056-4_091
%P 785-794

Markdown (Informal)

[Neural Feature Extraction for Contextual Emotion Detection](https://aclanthology.org/R19-1091) (Mohammadi et al., RANLP 2019)

• Neural Feature Extraction for Contextual Emotion Detection (Mohammadi et al., RANLP 2019)

ACL

• Elham Mohammadi, Hessam Amini, and Leila Kosseim. 2019. Neural Feature Extraction for Contextual Emotion Detection. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 785–794, Varna, Bulgaria. INCOMA Ltd..