jiesutd/LatticeLSTM

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1832 stars · 446 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of Lattice LSTM for Chinese Named Entity Recognition, accompanying an ACL 2018 paper. It combines character-based LSTM with lattice word embeddings and achieved state-of-the-art F1 on the MSRA dataset at publication time.

Use cases

• run Chinese named entity recognition on text
• train a Chinese NER model with lattice LSTM
• reproduce ACL 2018 Chinese NER paper results
• extract person, location, organization entities from Chinese text
• train NER on CoNLL-format BIOES data
• use pretrained character and word embeddings for Chinese NER

When to choose

• you need to reproduce or extend the ACL 2018 Lattice LSTM paper
• you work with legacy Python 2.7 / PyTorch 0.3 environments
• you want a research baseline for character-based Chinese NER with word-lattice features

When to avoid

• you need a maintained, production-ready NER toolkit
• you use modern PyTorch or Python 3
• you want transformer-based or multilingual NER models

Facets

library · maturity abandoned

nlp machine-learning deep-learning machine-learning deep-learning python named-entity-recognition lstm-crf lattice-lstm chinese-ner pytorch research-code acl-2018 natural-language-processing

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