@inproceedings{nguyen-etal-2019-podlab,
title = "Podlab at {S}em{E}val-2019 Task 3: The Importance of Being Shallow",
author = "Nguyen, Andrew and
South, Tobin and
Bean, Nigel and
Tuke, Jonathan and
Mitchell, Lewis",
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/S19-2050/",
doi = "10.18653/v1/S19-2050",
pages = "292--296",
abstract = "This paper describes our linear SVM system for emotion classification from conversational dialogue, entered in SemEval2019 Task 3. We used off-the-shelf tools coupled with feature engineering and parameter tuning to create a simple, interpretable, yet high-performing, classification model. Our system achieves a micro F1 score of 0.7357, which is 92\% of the top score for the competition, demonstrating that ``shallow'' classification approaches can perform well when coupled with detailed fea- ture selection and statistical analysis."
}
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<abstract>This paper describes our linear SVM system for emotion classification from conversational dialogue, entered in SemEval2019 Task 3. We used off-the-shelf tools coupled with feature engineering and parameter tuning to create a simple, interpretable, yet high-performing, classification model. Our system achieves a micro F1 score of 0.7357, which is 92% of the top score for the competition, demonstrating that “shallow” classification approaches can perform well when coupled with detailed fea- ture selection and statistical analysis.</abstract>
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%0 Conference Proceedings
%T Podlab at SemEval-2019 Task 3: The Importance of Being Shallow
%A Nguyen, Andrew
%A South, Tobin
%A Bean, Nigel
%A Tuke, Jonathan
%A Mitchell, Lewis
%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 nguyen-etal-2019-podlab
%X This paper describes our linear SVM system for emotion classification from conversational dialogue, entered in SemEval2019 Task 3. We used off-the-shelf tools coupled with feature engineering and parameter tuning to create a simple, interpretable, yet high-performing, classification model. Our system achieves a micro F1 score of 0.7357, which is 92% of the top score for the competition, demonstrating that “shallow” classification approaches can perform well when coupled with detailed fea- ture selection and statistical analysis.
%R 10.18653/v1/S19-2050
%U https://aclanthology.org/S19-2050/
%U https://doi.org/10.18653/v1/S19-2050
%P 292-296
Markdown (Informal)
[Podlab at SemEval-2019 Task 3: The Importance of Being Shallow](https://aclanthology.org/S19-2050/) (Nguyen et al., SemEval 2019)
ACL