Papers › Chinese NER Using Lattice LSTM
Chinese NER Using Lattice LSTM
Yue Zhang, Jie Yang
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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Code Syntology ran Syntology
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Chinese Named Entity Recognition | MSRA | Lattice | F1 | 93.18 | #19 of 21 | Archive leaderboard | report |
| Chinese Named Entity Recognition | OntoNotes 4 | Lattice | F1 | 73.88 | #14 of 15 | Archive leaderboard | report |
| Chinese Named Entity Recognition | Resume NER | Lattice | F1 | 94.46 | #13 of 13 | Archive leaderboard | report |
| Chinese Named Entity Recognition | Weibo NER | Lattice | F1 | 58.79 | #15 of 18 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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