@inproceedings{jain-etal-2019-paralleldots,
title = "{P}arallel{D}ots at {S}em{E}val-2019 Task 3: Domain Adaptation with feature embeddings for Contextual Emotion Analysis",
author = "Jain, Akansha and
Aggarwal, Ishita and
Singh, Ankit",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2029",
doi = "10.18653/v1/S19-2029",
pages = "185--189",
abstract = "This paper describes our proposed system {\&} experiments performed to detect contextual emotion in texts for SemEval 2019 Task 3. We exploit sentiment information, syntactic patterns {\&} semantic relatedness to capture diverse aspects of the text. Word level embeddings such as Glove, FastText, Emoji along with sentence level embeddings like Skip-Thought, DeepMoji {\&} Unsupervised Sentiment Neuron were used as input features to our architecture. We democratize the learning using ensembling of models with different parameters to produce the final output. This paper discusses comparative analysis of the significance of these embeddings and our approach for the task.",
}
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%0 Conference Proceedings
%T ParallelDots at SemEval-2019 Task 3: Domain Adaptation with feature embeddings for Contextual Emotion Analysis
%A Jain, Akansha
%A Aggarwal, Ishita
%A Singh, Ankit
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F jain-etal-2019-paralleldots
%X This paper describes our proposed system & experiments performed to detect contextual emotion in texts for SemEval 2019 Task 3. We exploit sentiment information, syntactic patterns & semantic relatedness to capture diverse aspects of the text. Word level embeddings such as Glove, FastText, Emoji along with sentence level embeddings like Skip-Thought, DeepMoji & Unsupervised Sentiment Neuron were used as input features to our architecture. We democratize the learning using ensembling of models with different parameters to produce the final output. This paper discusses comparative analysis of the significance of these embeddings and our approach for the task.
%R 10.18653/v1/S19-2029
%U https://aclanthology.org/S19-2029
%U https://doi.org/10.18653/v1/S19-2029
%P 185-189
Markdown (Informal)
[ParallelDots at SemEval-2019 Task 3: Domain Adaptation with feature embeddings for Contextual Emotion Analysis](https://aclanthology.org/S19-2029) (Jain et al., SemEval 2019)
ACL