cotitan / cctner

Chinese Clinical Text Named Entity Recognition

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1. Set up and Run the Demo

Install CRF++

If Mac, install crf++ at first.

By:

$ brew install crf++ 

If Window, the crf++ package is included in this project. Don't need to install it.

Train a Model

# generate a model 1a-v50
$ python train.py -m 1a -v vect-50

# generate a model 1abdp
$ python train.py -m 1abdp

# generate a model 2abp
$ python train.py -m 2abp

Tagger a Text File with a Model

$ python tagger.py -m 1a-v50 -i demo/input.txt -o demo/output.txt

Loading Model...
Text Input:
--------------------------
今日查房,患者精神、饮食、睡眠可,二便正常,自诉右膝内侧疼痛减轻、活动轻度受限,查体:一般情况可,心肺腹查体未见异常。右膝内侧、右小腿、右踝轻度肿胀略消退,压痛阳性。右膝关节稳定性可。患者要求出院,办理出院。
--------------------------

Entity Result:

+----+------------+-----------+---------+----------+
|    | E-Name     | E-Start   | E-End   | E-Type   |
|----+------------+-----------+---------+----------|
| 0  | 二便       | 17        | 18      | Bo       |
| 1  | 右膝内侧   | 24        | 27      | Bo       |
| 2  | 疼痛       | 28        | 29      | Sy       |
| 3  | 查体       | 40        | 41      | Ch       |
| 4  | 心肺腹查体 | 49        | 53      | Ch       |
| 5  | 右膝       | 59        | 60      | Bo       |
| 6  | 右小腿     | 64        | 66      | Bo       |
| 7  | 右踝       | 68        | 69      | Bo       |
| 8  | 压痛       | 78        | 79      | Ch       |
| 9  | 右膝关节   | 83        | 86      | Bo       |
+----+------------+-----------+---------+----------+

Time Consumption 0.021360 s

2. Workflows of this Project

3. Data Processing

4. Understand CRF Model

5. Usage of CRF++

6. Train Models with Configuration

7. Tag Texts with Learned Model

About

Chinese Clinical Text Named Entity Recognition


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Language:Jupyter Notebook 94.0%Language:Python 6.0%