# N-LTP: An Open-source Neural Language Technology Platform for Chinese

arXiv: https://arxiv.org/abs/2009.11616 · HTML: https://mlsanity.com/paper/2009.11616/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 29 of 29)

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- [SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation](https://mlsanity.com/paper/2412.11652/) (2024)
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- [Parsing Through Boundaries in Chinese Word Segmentation](https://mlsanity.com/paper/2503.23091/) (2025)
- [ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval](https://mlsanity.com/paper/2505.08130/) (2025)
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- [Semantic-Enhanced Cross-Modal Place Recognition for Robust Robot Localization](https://mlsanity.com/paper/2509.13474/) (2025)
- [CL2GEC: A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correction](https://mlsanity.com/paper/2509.13672/) (2025)
- [Investigating the Impact of Rationales for LLMs on Natural Language Understanding](https://mlsanity.com/paper/2510.16686/) (2025)
- [Quantity Convergence, Quality Divergence: Disentangling Fluency and Accuracy in L2 Mandarin Prosody](https://mlsanity.com/paper/2602.23071/) (2026)
- [Bidirectional Chinese and English Passive Sentences Dataset for Machine Translation](https://mlsanity.com/paper/2603.15227/) (2026)
- [Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic LLM-Teacher Collaboration for K-12 Writing at Scale](https://mlsanity.com/paper/2605.30200/) (2026)
- [A Layered Taxonomy for Chinese Learner Grammatical Error Annotation](https://mlsanity.com/paper/2609.02153/) (2026)

## Abstract (arXiv)

We introduce textttN-LTP, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: lexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition), syntactic parsing (dependency parsing), and semantic parsing (semantic dependency parsing and semantic role labeling). Unlike the existing state-of-the-art toolkits, such as textttStanza, that adopt an independent model for each task, textttN-LTP adopts the multi-task framework by using a shared pre-trained model, which has the advantage of capturing the shared knowledge across relevant Chinese tasks. In addition, a knowledge distillation method citeDBLP:journals/corr/abs-1907-04829 where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher. Finally, we provide a collection of easy-to-use APIs and a visualization tool to make users to use and view the processing results more easily and directly. To the best of our knowledge, this is the first toolkit to support six Chinese NLP fundamental tasks. Source code, documentation, and pre-trained models are available at urlhttps://github.com/HIT-SCIR/ltp.