GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus

Shabnam Tafreshi, Mona Diab


Abstract
In this paper we present an emotion classifier models that submitted to the SemEval-2019 Task 3 : EmoContext. Our approach is a Gated Recurrent Neural Network (GRU) model with attention layer is bootstrapped with contextual information and trained with a multigenre corpus, which is combination of several popular emotional data sets. We utilize different word embeddings to empirically select the most suited embedding to represent our features. Our aim is to build a robust emotion classifier that can generalize emotion detection, which is to learn emotion cues in a noisy training environment. To fulfill this aim we train our model with a multigenre emotion corpus, this way we leverage from having more training set. We achieved overall %56.05 f1-score and placed 144. Given our aim and noisy training environment, the results are anticipated.
Anthology ID:
S19-2038
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
230–235
Language:
URL:
https://aclanthology.org/S19-2038
DOI:
10.18653/v1/S19-2038
Bibkey:
Cite (ACL):
Shabnam Tafreshi and Mona Diab. 2019. GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 230–235, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus (Tafreshi & Diab, SemEval 2019)
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PDF:
https://aclanthology.org/S19-2038.pdf