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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W18-3215.pdf