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

Utpal Kumar Sikdar, Biswanath Barik, Björn Gambäck


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)
Copy Citation:
PDF:
https://aclanthology.org/W18-3215.pdf