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https://github.com/determined22/zh-ner-tf
A very simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (TensorFlow)
https://github.com/determined22/zh-ner-tf
bilstm-crf-model named-entity-recognition tensorflow
Last synced: 6 days ago
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A very simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (TensorFlow)
- Host: GitHub
- URL: https://github.com/determined22/zh-ner-tf
- Owner: Determined22
- Created: 2017-08-22T07:25:32.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-04-18T23:01:50.000Z (almost 3 years ago)
- Last Synced: 2025-02-01T03:43:46.219Z (6 days ago)
- Topics: bilstm-crf-model, named-entity-recognition, tensorflow
- Language: Python
- Homepage:
- Size: 107 MB
- Stars: 2,332
- Watchers: 61
- Forks: 937
- Open Issues: 78
-
Metadata Files:
- Readme: README.md
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README
# A simple BiLSTM-CRF model for Chinese Named Entity Recognition
This repository includes the code for buliding a very simple __character-based BiLSTM-CRF sequence labeling model__ for Chinese Named Entity Recognition task. Its goal is to recognize three types of Named Entity: PERSON, LOCATION and ORGANIZATION.
This code works on __Python 3 & TensorFlow 1.2__ and the following repository [https://github.com/guillaumegenthial/sequence_tagging](https://github.com/guillaumegenthial/sequence_tagging) gives me much help.
## Model
This model is similar to the models provided by paper [1] and [2]. Its structure looks just like the following illustration:
For one Chinese sentence, each character in this sentence has / will have a tag which belongs to the set {O, B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG}.
The first layer, __look-up layer__, aims at transforming each character representation from one-hot vector into *character embedding*. In this code I initialize the embedding matrix randomly. We could add some linguistic knowledge later. For example, do tokenization and use pre-trained word-level embedding, then augment character embedding with the corresponding token's word embedding. In addition, we can get the character embedding by combining low-level features (please see paper[2]'s section 4.1 and paper[3]'s section 3.3 for more details).
The second layer, __BiLSTM layer__, can efficiently use *both past and future* input information and extract features automatically.
The third layer, __CRF layer__, labels the tag for each character in one sentence. If we use a Softmax layer for labeling, we might get ungrammatic tag sequences beacuse the Softmax layer labels each position independently. We know that 'I-LOC' cannot follow 'B-PER' but Softmax doesn't know. Compared to Softmax, a CRF layer can use *sentence-level tag information* and model the transition behavior of each two different tags.
## Dataset
| | #sentence | #PER | #LOC | #ORG |
| :----: | :---: | :---: | :---: | :---: |
| train | 46364 | 17615 | 36517 | 20571 |
| test | 4365 | 1973 | 2877 | 1331 |
It looks like a portion of [MSRA corpus](http://sighan.cs.uchicago.edu/bakeoff2006/). I downloaded the dataset from the link in `./data_path/original/link.txt`
### data files
The directory `./data_path` contains:
- the preprocessed data files, `train_data` and `test_data`
- a vocabulary file `word2id.pkl` that maps each character to a unique id
For generating vocabulary file, please refer to the code in `data.py`.
### data format
Each data file should be in the following format:
```
中 B-LOC
国 I-LOC
很 O
大 O
句 O
子 O
结 O
束 O
是 O
空 O
行 O
```
If you want to use your own dataset, please:
- transform your corpus to the above format
- generate a new vocabulary file
## How to Run
### train
`python main.py --mode=train `
### test
`python main.py --mode=test --demo_model=1521112368`
Please set the parameter `--demo_model` to the model that you want to test. `1521112368` is the model trained by me.
An official evaluation tool for computing metrics: [here (click 'Instructions')](http://sighan.cs.uchicago.edu/bakeoff2006/)
My test performance:
| P | R | F | F (PER)| F (LOC)| F (ORG)|
| :---: | :---: | :---: | :---: | :---: | :---: |
| 0.8945 | 0.8752 | 0.8847 | 0.8688 | 0.9118 | 0.8515
### demo
`python main.py --mode=demo --demo_model=1521112368`
You can input one Chinese sentence and the model will return the recognition result:
## Reference
\[1\] [Bidirectional LSTM-CRF Models for Sequence Tagging](https://arxiv.org/pdf/1508.01991v1.pdf)
\[2\] [Neural Architectures for Named Entity Recognition](http://aclweb.org/anthology/N16-1030)
\[3\] [Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition](https://link.springer.com/chapter/10.1007/978-3-319-50496-4_20)
\[4\] [https://github.com/guillaumegenthial/sequence_tagging](https://github.com/guillaumegenthial/sequence_tagging)