  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.
