ltp

Why we included this project

LTP pulls the usual Chinese NLP pipeline into one model: word segmentation, part-of-speech tagging, named entity recognition, dependency parsing, and semantic role labeling all come from a shared pre-trained checkpoint. Because the tasks are trained together, a single pass produces the related annotations at once, which tends to be faster and more accurate than wiring up separate single-purpose models. There's also an older perceptron-based engine, reimplemented in Rust, that trades a bit of accuracy for much higher throughput on big corpora. Python developers get a pipeline API, Rust users get a native interface, and models auto-download from Hugging Face Hub. Training scripts and custom vocabulary support let teams fine-tune the checkpoints on their own labeled data instead of treating them as fixed.