KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation

Nourah Alswaidan, Mohamed El Bachir Menai

Abstract
We proposed a model to address emotion recognition in textual conversation based on using automatically extracted features and human engineered features. The proposed model utilizes a fast gated-recurrent-unit backed by CuDNN, and a convolutional neural network to automatically extract features. The human engineered features take the frequency-inverse document frequency of semantic meaning and mood tags extracted from SinticNet.
Anthology ID:
S19-2041
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
247–250
Language:
URL:
https://aclanthology.org/S19-2041
DOI:
10.18653/v1/S19-2041
Bibkey:
Cite (ACL):
Nourah Alswaidan and Mohamed El Bachir Menai. 2019. KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 247–250, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation (Alswaidan & Menai, SemEval 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/paclic-22-ingestion/S19-2041.pdf
PDF Search