@inproceedings{perez-luque-2019-atalaya,
title = "Atalaya at {S}em{E}val 2019 Task 5: Robust Embeddings for Tweet Classification",
author = "P{\'e}rez, Juan Manuel and
Luque, Franco M.",
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/S19-2008/",
doi = "10.18653/v1/S19-2008",
pages = "64--69",
abstract = "In this article, we describe our participation in HatEval, a shared task aimed at the detection of hate speech against immigrants and women. We focused on Spanish subtasks, building from our previous experiences on sentiment analysis in this language. We trained linear classifiers and Recurrent Neural Networks, using classic features, such as bag-of-words, bag-of-characters, and word embeddings, and also with recent techniques such as contextualized word representations. In particular, we trained robust task-oriented subword-aware embeddings and computed tweet representations using a weighted-averaging strategy. In the final evaluation, our systems showed competitive results for both Spanish subtasks ES-A and ES-B, achieving the first and fourth places respectively."
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<abstract>In this article, we describe our participation in HatEval, a shared task aimed at the detection of hate speech against immigrants and women. We focused on Spanish subtasks, building from our previous experiences on sentiment analysis in this language. We trained linear classifiers and Recurrent Neural Networks, using classic features, such as bag-of-words, bag-of-characters, and word embeddings, and also with recent techniques such as contextualized word representations. In particular, we trained robust task-oriented subword-aware embeddings and computed tweet representations using a weighted-averaging strategy. In the final evaluation, our systems showed competitive results for both Spanish subtasks ES-A and ES-B, achieving the first and fourth places respectively.</abstract>
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%0 Conference Proceedings
%T Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification
%A Pérez, Juan Manuel
%A Luque, Franco M.
%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 perez-luque-2019-atalaya
%X In this article, we describe our participation in HatEval, a shared task aimed at the detection of hate speech against immigrants and women. We focused on Spanish subtasks, building from our previous experiences on sentiment analysis in this language. We trained linear classifiers and Recurrent Neural Networks, using classic features, such as bag-of-words, bag-of-characters, and word embeddings, and also with recent techniques such as contextualized word representations. In particular, we trained robust task-oriented subword-aware embeddings and computed tweet representations using a weighted-averaging strategy. In the final evaluation, our systems showed competitive results for both Spanish subtasks ES-A and ES-B, achieving the first and fourth places respectively.
%R 10.18653/v1/S19-2008
%U https://aclanthology.org/S19-2008/
%U https://doi.org/10.18653/v1/S19-2008
%P 64-69
Markdown (Informal)
[Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification](https://aclanthology.org/S19-2008/) (Pérez & Luque, SemEval 2019)
ACL