Chinese NER Using Lattice LSTM

Code

• github.com/jiesutd/LatticeLSTM
  OfficialIn paperpytorch★ 0
• github.com/Houlong66/lattice_lstm_with_pytorch
  pytorch★ 24
• github.com/LeeSureman/Batch_Parallel_LatticeLSTM
  pytorch★ 0

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.

Tasks

Reproductions