Named Entity Recognition on Code-Switched Data Using Conditional Random Fields

Utpal Kumar Sikdar, Biswanath Barik, Björn Gambäck

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Abstract
Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts and classifies them into some pre-defined categories. Identification of named entities in code-mixed social media texts is a more difficult and challenging task as the contexts are short, ambiguous and often noisy. This work proposes a Conditional Random Fields based named entity recognition system to identify proper names in code-switched data and classify them into nine categories. The system ranked fifth among nine participant systems and achieved a 59.25% F1-score.
Anthology ID:
W18-3215
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:
115–119
Language:
URL:
https://aclanthology.org/W18-3215
DOI:
10.18653/v1/W18-3215
Bibkey:
Cite (ACL):
Utpal Kumar Sikdar, Biswanath Barik, and Björn Gambäck. 2018. Named Entity Recognition on Code-Switched Data Using Conditional Random Fields. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 115–119, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Named Entity Recognition on Code-Switched Data Using Conditional Random Fields (Sikdar et al., ACL 2018)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W18-3215.pdf
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