Chinese NER Using Lattice LSTM
Yue Zhang, Jie Yang · 2018
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.