@inproceedings{cummings-wilson-2019-clark,
title = "{CLARK} at {S}em{E}val-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short Conversation",
author = "Cummings, Joseph and
Wilson, Jason",
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/S19-2024",
doi = "10.18653/v1/S19-2024",
pages = "159--163",
abstract = "With text lacking valuable information avail-able in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of context in recognizing emotion in a conversation. We use a Naive Bayes model to show that inferring the mood of the conversation before classifying individual utterances leads to better performance. Additionally, we find that using context while train-ing the model significantly decreases performance. Our approach has the additional bene-fit that its performance rivals a baseline LSTM model while requiring fewer resources.",
}
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<abstract>With text lacking valuable information avail-able in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of context in recognizing emotion in a conversation. We use a Naive Bayes model to show that inferring the mood of the conversation before classifying individual utterances leads to better performance. Additionally, we find that using context while train-ing the model significantly decreases performance. Our approach has the additional bene-fit that its performance rivals a baseline LSTM model while requiring fewer resources.</abstract>
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%0 Conference Proceedings
%T CLARK at SemEval-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short Conversation
%A Cummings, Joseph
%A Wilson, Jason
%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 cummings-wilson-2019-clark
%X With text lacking valuable information avail-able in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of context in recognizing emotion in a conversation. We use a Naive Bayes model to show that inferring the mood of the conversation before classifying individual utterances leads to better performance. Additionally, we find that using context while train-ing the model significantly decreases performance. Our approach has the additional bene-fit that its performance rivals a baseline LSTM model while requiring fewer resources.
%R 10.18653/v1/S19-2024
%U https://aclanthology.org/S19-2024
%U https://doi.org/10.18653/v1/S19-2024
%P 159-163
Markdown (Informal)
[CLARK at SemEval-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short Conversation](https://aclanthology.org/S19-2024) (Cummings & Wilson, SemEval 2019)
ACL