Chinese NER Using Lattice LSTM

Yue Zhang; Jie Yang

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arXiv · 1805.02023

Chinese NER Using Lattice LSTM

Yue Zhang·Jie Yang

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Abstract

We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with character-based methods, our model explicitly leverages word and word sequence information. Compared with word-based methods, lattice LSTM does not suffer from segmentation errors. Gated recurrent cells allow our model to choose the most relevant characters and words from a sentence for better NER results. Experiments on various datasets show that lattice LSTM outperforms both word-based and character-based LSTM baselines, achieving the best results.

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BibTeX RIS

Yue Zhang, Jie Yang. 2018-05-05. Chinese NER Using Lattice LSTM. https://arxiv.org/abs/1805.02023

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