@inproceedings{aguilar-etal-2018-named,
title = "Named Entity Recognition on Code-Switched Data: Overview of the {CALCS} 2018 Shared Task",
author = "Aguilar, Gustavo and
AlGhamdi, Fahad and
Soto, Victor and
Diab, Mona and
Hirschberg, Julia and
Solorio, Thamar",
editor = "Aguilar, Gustavo and
AlGhamdi, Fahad and
Soto, Victor and
Solorio, Thamar and
Diab, Mona and
Hirschberg, Julia",
booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3219/",
doi = "10.18653/v1/W18-3219",
pages = "138--147",
abstract = "In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76\% and 71.61\% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions."
}
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%0 Conference Proceedings
%T Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
%A Aguilar, Gustavo
%A AlGhamdi, Fahad
%A Soto, Victor
%A Diab, Mona
%A Hirschberg, Julia
%A Solorio, Thamar
%Y Aguilar, Gustavo
%Y AlGhamdi, Fahad
%Y Soto, Victor
%Y Solorio, Thamar
%Y Diab, Mona
%Y Hirschberg, Julia
%S Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F aguilar-etal-2018-named
%X In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.
%R 10.18653/v1/W18-3219
%U https://aclanthology.org/W18-3219/
%U https://doi.org/10.18653/v1/W18-3219
%P 138-147
Markdown (Informal)
[Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task](https://aclanthology.org/W18-3219/) (Aguilar et al., ACL 2018)
ACL