Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task

Gustavo Aguilar, Fahad AlGhamdi, Victor Soto, Mona Diab, Julia Hirschberg, Thamar Solorio

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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.
Anthology ID:
W18-3219
Volume:
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Gustavo Aguilar, Fahad AlGhamdi, Victor Soto, Thamar Solorio, Mona Diab, Julia Hirschberg
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
138–147
Language:
URL:
https://aclanthology.org/W18-3219
DOI:
10.18653/v1/W18-3219
Bibkey:
Cite (ACL):
Gustavo Aguilar, Fahad AlGhamdi, Victor Soto, Mona Diab, Julia Hirschberg, and Thamar Solorio. 2018. Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 138–147, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task (Aguilar et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W18-3219.pdf
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