@inproceedings{bestgen-2019-cecl,
title = "{CECL} at {S}em{E}val-2019 Task 3: Using Surface Learning for Detecting Emotion in Textual Conversations",
author = "Bestgen, Yves",
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/S19-2022/",
doi = "10.18653/v1/S19-2022",
pages = "148--152",
abstract = "This paper describes the system developed by the Centre for English Corpus Linguistics for the SemEval-2019 Task 3: EmoContext. It aimed at classifying the emotion of a user utterance in a textual conversation as happy, sad, angry or other. It is based on a large number of feature types, mainly unigrams and bigrams, which were extracted by a SAS program. The usefulness of the different feature types was evaluated by means of Monte-Carlo resampling tests. As this system does not rest on any deep learning component, which is currently considered as the state-of-the-art approach, it can be seen as a possible point of comparison for such kind of systems."
}
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%0 Conference Proceedings
%T CECL at SemEval-2019 Task 3: Using Surface Learning for Detecting Emotion in Textual Conversations
%A Bestgen, Yves
%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 bestgen-2019-cecl
%X This paper describes the system developed by the Centre for English Corpus Linguistics for the SemEval-2019 Task 3: EmoContext. It aimed at classifying the emotion of a user utterance in a textual conversation as happy, sad, angry or other. It is based on a large number of feature types, mainly unigrams and bigrams, which were extracted by a SAS program. The usefulness of the different feature types was evaluated by means of Monte-Carlo resampling tests. As this system does not rest on any deep learning component, which is currently considered as the state-of-the-art approach, it can be seen as a possible point of comparison for such kind of systems.
%R 10.18653/v1/S19-2022
%U https://aclanthology.org/S19-2022/
%U https://doi.org/10.18653/v1/S19-2022
%P 148-152
Markdown (Informal)
[CECL at SemEval-2019 Task 3: Using Surface Learning for Detecting Emotion in Textual Conversations](https://aclanthology.org/S19-2022/) (Bestgen, SemEval 2019)
ACL