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arXiv · 1805.12061

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
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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.

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Genta Indra Winata, Chien-Sheng Wu, Andrea Madotto, Pascale Fung. 2019-06-10. Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition. https://arxiv.org/abs/1805.12061

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