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

arXiv: https://arxiv.org/abs/1805.12061 · HTML: https://mlsanity.com/paper/1805.12061/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2018.

## Built on by (most cited first, 4 of 4)

- [Are Multilingual Models Effective in Code-Switching?](https://mlsanity.com/paper/2103.13309/) (2021)
- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages](https://mlsanity.com/paper/2507.18791/) (2025)
- [How Effective is Incongruity? Implications for Code-mix Sarcasm Detection](https://mlsanity.com/paper/2202.02702/) (2022)

## Abstract (arXiv)

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.