Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition

Genta Indra Winata, Chien-Sheng Wu, Andrea Madotto, Pascale Fung


Abstract
We propose an LSTM-based model with hierarchical architecture on named entity recognition from code-switching Twitter data. Our model uses bilingual character representation and transfer learning to address out-of-vocabulary words. In order to mitigate data noise, we propose to use token replacement and normalization. In the 3rd Workshop on Computational Approaches to Linguistic Code-Switching Shared Task, we achieved second place with 62.76% harmonic mean F1-score for English-Spanish language pair without using any gazetteer and knowledge-based information.
Anthology ID:
W18-3214
Volume:
Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–114
Language:
URL:
https://aclanthology.org/W18-3214
DOI:
10.18653/v1/W18-3214
Bibkey:
Cite (ACL):
Genta Indra Winata, Chien-Sheng Wu, Andrea Madotto, and Pascale Fung. 2018. Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 110–114, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition (Winata et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/paclic-22-ingestion/W18-3214.pdf