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 · 2018
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.