https://github.com/jiesutd/LatticeLSTM

Chinese NER using Lattice LSTM. Code for ACL 2018 paper.
https://github.com/jiesutd/LatticeLSTM

chinese-ner lattice-lstm lattice-lstm-crf lstm-crf ner

Last synced: about 1 month ago
JSON representation

Chinese NER using Lattice LSTM. Code for ACL 2018 paper.

• Host: GitHub
• URL: https://github.com/jiesutd/LatticeLSTM
• Owner: jiesutd
• Created: 2018-04-22T14:23:20.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-25T14:44:32.000Z (over 5 years ago)
• Last Synced: 2024-11-05T08:42:28.322Z (about 2 months ago)
• Topics: chinese-ner, lattice-lstm, lattice-lstm-crf, lstm-crf, ner
• Language: Python
• Homepage:
• Size: 330 KB
• Stars: 1,795
• Watchers: 42
• Forks: 453
• Open Issues: 12
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• StarryDivineSky - jiesutd/LatticeLSTM

README

        
Chinese NER Using Lattice LSTM

====

Lattice LSTM for Chinese NER. Character based LSTM with Lattice embeddings as input.

Models and results can be found at our ACL 2018 paper [Chinese NER Using Lattice LSTM](https://arxiv.org/pdf/1805.02023.pdf). It achieves 93.18% F1-value on MSRA dataset, which is the state-of-the-art result on Chinese NER task.

Details will be updated soon.

Requirement:

======

	Python: 2.7   

	PyTorch: 0.3.0 

(for PyTorch 0.3.1, please refer [issue#8](https://github.com/jiesutd/LatticeLSTM/issues/8) for a slight modification.)

Input format:

======

CoNLL format (prefer BIOES tag scheme), with each character its label for one line. Sentences are splited with a null line.

	美	B-LOC

	国	E-LOC

	的	O

	华	B-PER

	莱	I-PER

	士	E-PER

	我	O

	跟	O

	他	O

	谈	O

	笑	O

	风	O

	生	O 

Pretrained Embeddings:

====

The pretrained character and word embeddings are the same with the embeddings in the baseline of [RichWordSegmentor](https://github.com/jiesutd/RichWordSegmentor)

Character embeddings (gigaword_chn.all.a2b.uni.ite50.vec): [Google Drive](https://drive.google.com/file/d/1_Zlf0OAZKVdydk7loUpkzD2KPEotUE8u/view?usp=sharing) or [Baidu Pan](https://pan.baidu.com/s/1pLO6T9D)

Word(Lattice) embeddings (ctb.50d.vec): [Google Drive](https://drive.google.com/file/d/1K_lG3FlXTgOOf8aQ4brR9g3R40qi1Chv/view?usp=sharing) or [Baidu Pan](https://pan.baidu.com/s/1pLO6T9D)

How to run the code?

====

1. Download the character embeddings and word embeddings and put them in the `data` folder.

2. Modify the `run_main.py` or `run_demo.py` by adding your train/dev/test file directory.

3. `sh run_main.py` or `sh run_demo.py`

Resume NER data 

====

Crawled from the Sina Finance, it includes the resumes of senior executives from listed companies in the Chinese stock market. Details can be found in our paper.

Cite: 

========

Please cite our ACL 2018 paper:

    @article{zhang2018chinese,  

     title={Chinese NER Using Lattice LSTM},  

     author={Yue Zhang and Jie Yang},  

     booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL)},

     year={2018}  

    }