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مشاهده بیشترتوضیحات
A package for NER evaluation
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | eval4ner-0.1.0 |
| نام | eval4ner |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | cyk1337 |
| ایمیل نویسنده | chaiyekun@gmail.com |
| آدرس صفحه اصلی | https://github.com/cyk1337/eval4ner |
| آدرس اینترنتی | https://pypi.org/project/eval4ner/ |
| مجوز | - |
# eval4ner: An All-Round Evaluation for Named Entity Recognition
Table of Contents
=================
- [TL;DR](https://github.com/cyk1337/eval4ner/#tldr)
- [Preliminaries for NER Evaluation](https://github.com/cyk1337/eval4ner/#preliminaries-for-ner-evaluation)
- [User Guide](https://github.com/cyk1337/eval4ner/#user-guide)
- [Installation](https://github.com/cyk1337/eval4ner/#installation)
- [Usage](https://github.com/cyk1337/eval4ner/#usage)
- [Citation](https://github.com/cyk1337/eval4ner/#citation)
- [References](https://github.com/cyk1337/eval4ner/#references)
This is a Python toolkit of MUC-5 evaluation metrics for evaluating Named Entity Recognition (NER) results.
## TL;DR
It considers not only the mode of strict matching, *i.e.*, extracted entities are correct w.r.t both boundaries and types, but that of partial match, summarizing as following four modes:
- Strict：exact match (Both entity boundary and type are correct)
- Exact boundary matching：predicted entity boundary is correct, regardless of entity boundary
- Partial boundary matching：entity boundaries overlap, regardless of entity boundary
- Type matching：some overlap between the system tagged entity and the gold annotation is required;
Refer to the blog [Evaluation Metrics of Name Entity Recognition](https://ychai.uk/notes/2018/11/21/NLP/NER/NER-Evaluation-Metrics/#SemEval%E2%80%9813) for explanations of MUC metric.
## Preliminaries for NER Evaluation
In research and production, following scenarios of NER systems can occur frequently:
<table class="tg">
<tr>
<th class="tg-0pky">Scenario</th>
<th class="tg-c3ow" colspan="2">Golden Standard</th>
<th class="tg-c3ow" colspan="2">NER system prediction</th>
<th class="tg-c3ow" colspan="4">Measure</th>
</tr>
<tr>
<td class="tg-0pky"></td>
<td class="tg-c3ow">Entity Type</td>
<td class="tg-c3ow">Entity Boundary (Surface String)</td>
<td class="tg-0pky">Entity Type</td>
<td class="tg-0pky">Entity Boundary (Surface String)</td>
<td class="tg-0pky">Type</td>
<td class="tg-0pky">Partial</td>
<td class="tg-0pky">Exact</td>
<td class="tg-0pky">Strict</td>
</tr>
<tr>
<td class="tg-0pky">III</td>
<td class="tg-c3ow">MUSIC_NAME</td>
<td class="tg-c3ow">告白气球</td>
<td class="tg-0pky"></td>
<td class="tg-0pky"></td>
<td class="tg-0pky">MIS</td>
<td class="tg-0pky">MIS</td>
<td class="tg-0pky">MIS</td>
<td class="tg-0pky">MIS</td>
</tr>
<tr>
<td class="tg-0pky">II</td>
<td class="tg-c3ow"></td>
<td class="tg-c3ow"></td>
<td class="tg-0pky">MUSIC_NAME</td>
<td class="tg-0pky">年轮</td>
<td class="tg-0pky">SPU</td>
<td class="tg-0pky">SPU</td>
<td class="tg-0pky">SPU</td>
<td class="tg-0pky">SPU</td>
</tr>
<tr>
<td class="tg-0pky">V</td>
<td class="tg-c3ow">MUSIC_NAME</td>
<td class="tg-c3ow">告白气球</td>
<td class="tg-0pky">MUSIC_NAME</td>
<td class="tg-0pky">一首告白气球</td>
<td class="tg-0pky">COR</td>
<td class="tg-0pky">PAR</td>
<td class="tg-0pky">INC</td>
<td class="tg-0pky">INC</td>
</tr>
<tr>
<td class="tg-0pky">IV</td>
<td class="tg-c3ow">MUSIC_NAME</td>
<td class="tg-c3ow">告白气球</td>
<td class="tg-0pky">SINGER</td>
<td class="tg-0pky">告白气球</td>
<td class="tg-0pky">INC</td>
<td class="tg-0pky">COR</td>
<td class="tg-0pky">COR</td>
<td class="tg-0pky">INC</td>
</tr>
<tr>
<td class="tg-0pky">I</td>
<td class="tg-c3ow">MUSIC_NAME</td>
<td class="tg-c3ow">告白气球</td>
<td class="tg-0pky">MUSIC_NAME</td>
<td class="tg-0pky">告白气球</td>
<td class="tg-0pky">COR</td>
<td class="tg-0pky">COR</td>
<td class="tg-0pky">COR</td>
<td class="tg-0pky">COR</td>
</tr>
<tr>
<td class="tg-0pky">VI</td>
<td class="tg-c3ow">MUSIC_NAME</td>
<td class="tg-c3ow">告白气球</td>
<td class="tg-0pky">SINGER</td>
<td class="tg-0pky">一首告白气球</td>
<td class="tg-0pky">INC</td>
<td class="tg-0pky">PAR</td>
<td class="tg-0pky">INC</td>
<td class="tg-0pky">INC</td>
</tr>
</table>
Thus, MUC-5 takes into account all these scenarios for all-sided evaluation.
Then we can compute:
**Number of golden standard**:
<img src="https://render.githubusercontent.com/render/math?math=Possible(POS) = COR %2B INC %2B PAR %2B MIS = TP %2B FN">
**Number of predictee**:
<img src="https://render.githubusercontent.com/render/math?math=Actual(ACT) = COR %2B INC %2B PAR %2B SPU = TP %2B FP">
The evaluation type of exact match and partial match are as follows:
### Exact match(i.e. Strict, Exact)
<img src="https://render.githubusercontent.com/render/math?math=Precision = \frac{COR}{ACT} = \frac{TP}{TP%2BFP}">
<img src="https://render.githubusercontent.com/render/math?math=Recall =\frac{COR}{POS}=\frac{TP}{TP%2BFN}">
### Partial match (i.e. Partial, Type)
<img src="https://render.githubusercontent.com/render/math?math=Precision = \frac{COR %2B 0.5\times PAR}{ACT}">
<img src="https://render.githubusercontent.com/render/math?math=Recall = \frac{COR %2B 0.5 \times PAR}{POS}">
### F-Measure
<img src="https://render.githubusercontent.com/render/math?math=F_\alpha = \frac{(\alpha^2 %2B 1)PR}{\alpha^2 P%2BR}">
<img src="https://render.githubusercontent.com/render/math?math=F_1 = \frac{2PR}{P%2BR}">
Therefore, we can get the results:
<table class="tg">
<tr>
<th class="tg-e6bt">Measure</th>
<th class="tg-23iq">Type</th>
<th class="tg-23iq">Partial</th>
<th class="tg-ww3v">Exact</th>
<th class="tg-ww3v">Strict</th>
</tr>
<tr>
<td class="tg-e6bt">Correct</td>
<td class="tg-23iq">2</td>
<td class="tg-23iq">2</td>
<td class="tg-ww3v">2</td>
<td class="tg-ww3v">1</td>
</tr>
<tr>
<td class="tg-e6bt">Incorrect</td>
<td class="tg-23iq">2</td>
<td class="tg-23iq">0</td>
<td class="tg-ww3v">2</td>
<td class="tg-ww3v">3</td>
</tr>
<tr>
<td class="tg-e6bt">Partial</td>
<td class="tg-23iq">0</td>
<td class="tg-23iq">2</td>
<td class="tg-ww3v">0</td>
<td class="tg-ww3v">0</td>
</tr>
<tr>
<td class="tg-e6bt">Missed</td>
<td class="tg-23iq">1</td>
<td class="tg-23iq">1</td>
<td class="tg-ww3v">1</td>
<td class="tg-ww3v">1</td>
</tr>
<tr>
<td class="tg-e6bt">Spurius</td>
<td class="tg-23iq">1</td>
<td class="tg-23iq">1</td>
<td class="tg-ww3v">1</td>
<td class="tg-ww3v">1</td>
</tr>
<tr>
<td class="tg-e6bt">Precision</td>
<td class="tg-23iq">0.4</td>
<td class="tg-23iq">0.6</td>
<td class="tg-ww3v">0.4</td>
<td class="tg-ww3v">0.2</td>
</tr>
<tr>
<td class="tg-e6bt">Recall</td>
<td class="tg-23iq">0.4</td>
<td class="tg-23iq">0.6</td>
<td class="tg-ww3v">0.4</td>
<td class="tg-ww3v">0.2</td>
</tr>
<tr>
<td class="tg-gx32">F1 score</td>
<td class="tg-t0np">0.4</td>
<td class="tg-t0np">0.6</td>
<td class="tg-8l38">0.4</td>
<td class="tg-8l38">0.2</td>
</tr>
</table>
## User Guide
### Installation
```bash
pip install [-U] eval4ner
```
### Usage
#### 1. Evaluate single prediction
```python
import eval4ner.muc as muc
import pprint
grount_truth = [('PER', 'John Jones'), ('PER', 'Peter Peters'), ('LOC', 'York')]
prediction = [('PER', 'John Jones and Peter Peters came to York')]
text = 'John Jones and Peter Peters came to York'
one_result = muc.evaluate_one(prediction, grount_truth, text)
pprint.pprint(one_result)
```
Output:
```bash
{'exact': {'actual': 1,
'correct': 0,
'f1_score': 0,
'incorrect': 1,
'missed': 2,
'partial': 0,
'possible': 3,
'precision': 0.0,
'recall': 0.0,
'spurius': 0},
'partial': {'actual': 1,
'correct': 0,
'f1_score': 0.25,
'incorrect': 0,
'missed': 2,
'partial': 1,
'possible': 3,
'precision': 0.5,
'recall': 0.16666666666666666,
'spurius': 0},
'strict': {'actual': 1,
'correct': 0,
'f1_score': 0,
'incorrect': 1,
'missed': 2,
'partial': 0,
'possible': 3,
'precision': 0.0,
'recall': 0.0,
'spurius': 0},
'type': {'actual': 1,
'correct': 1,
'f1_score': 0.5,
'incorrect': 0,
'missed': 2,
'partial': 0,
'possible': 3,
'precision': 1.0,
'recall': 0.3333333333333333,
'spurius': 0}}
```
#### 2. Evaluate all predictions
```python
import eval4ner.muc as muc
# ground truth
grount_truths = [
[('PER', 'John Jones'), ('PER', 'Peter Peters'), ('LOC', 'York')],
[('PER', 'John Jones'), ('PER', 'Peter Peters'), ('LOC', 'York')],
[('PER', 'John Jones'), ('PER', 'Peter Peters'), ('LOC', 'York')]
]
# NER model prediction
predictions = [
[('PER', 'John Jones and Peter Peters came to York')],
[('LOC', 'John Jones'), ('PER', 'Peters'), ('LOC', 'York')],
[('PER', 'John Jones'), ('PER', 'Peter Peters'), ('LOC', 'York')]
]
# input texts
texts = [
'John Jones and Peter Peters came to York',
'John Jones and Peter Peters came to York',
'John Jones and Peter Peters came to York'
]
muc.evaluate_all(predictions, grount_truths * 1, texts, verbose=True)
```
Output:
```bash
NER evaluation scores:
strict mode, Precision=0.4444, Recall=0.4444, F1:0.4444
exact mode, Precision=0.5556, Recall=0.5556, F1:0.5556
partial mode, Precision=0.7778, Recall=0.6667, F1:0.6944
type mode, Precision=0.8889, Recall=0.6667, F1:0.7222
```
This repo will be long-term supported. Welcome to contribute and PR.
## Citation
For attribution in academic contexts, please cite this work as:
```
@misc{eval4ner,
title={Evaluation Metrics of Named Entity Recognition},
author={Chai, Yekun},
year={2018},
howpublished={\url{https://cyk1337.github.io/notes/2018/11/21/NLP/NER/NER-Evaluation-Metrics/}},
}
@misc{chai2018-ner-eval,
author = {Chai, Yekun},
title = {eval4ner: An All-Round Evaluation for Named Entity Recognition},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/cyk1337/eval4ner}}
}
```
## References
1. [Evaluation of the SemEval-2013 Task 9.1: Recognition and Classification of pharmacological substances](https://www.cs.york.ac.uk/semeval-2013/task9/data/uploads/semeval_2013-task-9_1-evaluation-metrics.pdf)
2. [MUC-5 Evaluation Metrics](https://www.aclweb.org/anthology/M93-1007.pdf)
نحوه نصب
نصب پکیج whl eval4ner-0.1.0:
pip install eval4ner-0.1.0.whl
نصب پکیج tar.gz eval4ner-0.1.0:
pip install eval4ner-0.1.0.tar.gz