@inproceedings{kolchinski-potts-2018-representing,
title = "Representing Social Media Users for Sarcasm Detection",
author = "Kolchinski, Y. Alex and
Potts, Christopher",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1140",
doi = "10.18653/v1/D18-1140",
pages = "1115--1121",
abstract = "We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors{'} propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text. Using the SARC dataset of Reddit comments, we show that augmenting a bidirectional RNN with these representations improves performance; the Bayesian approach suffices in homogeneous contexts, whereas the added power of the dense embeddings proves valuable in more diverse ones.",
}
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%0 Conference Proceedings
%T Representing Social Media Users for Sarcasm Detection
%A Kolchinski, Y. Alex
%A Potts, Christopher
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kolchinski-potts-2018-representing
%X We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors’ propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text. Using the SARC dataset of Reddit comments, we show that augmenting a bidirectional RNN with these representations improves performance; the Bayesian approach suffices in homogeneous contexts, whereas the added power of the dense embeddings proves valuable in more diverse ones.
%R 10.18653/v1/D18-1140
%U https://aclanthology.org/D18-1140
%U https://doi.org/10.18653/v1/D18-1140
%P 1115-1121
Markdown (Informal)
[Representing Social Media Users for Sarcasm Detection](https://aclanthology.org/D18-1140) (Kolchinski & Potts, EMNLP 2018)
ACL
- Y. Alex Kolchinski and Christopher Potts. 2018. Representing Social Media Users for Sarcasm Detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1115–1121, Brussels, Belgium. Association for Computational Linguistics.