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https://github.com/jenojp/extractacy
Spacy pipeline object for extracting values that correspond to a named entity (e.g., birth dates, account numbers, laboratory results)
https://github.com/jenojp/extractacy
entity-extraction entity-linking ner nlp pattern-matching spacy spacy-extension spacy-pipeline
Last synced: 2 months ago
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Spacy pipeline object for extracting values that correspond to a named entity (e.g., birth dates, account numbers, laboratory results)
- Host: GitHub
- URL: https://github.com/jenojp/extractacy
- Owner: jenojp
- License: mit
- Created: 2020-02-29T17:33:12.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2022-05-25T20:22:11.000Z (over 2 years ago)
- Last Synced: 2024-04-14T10:48:16.633Z (8 months ago)
- Topics: entity-extraction, entity-linking, ner, nlp, pattern-matching, spacy, spacy-extension, spacy-pipeline
- Language: Python
- Size: 124 KB
- Stars: 52
- Watchers: 3
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# extractacy - pattern extraction and named entity linking for spaCy
[](https://dev.azure.com/jenopizzaro/extractacy/_build/latest?definitionId=3&branchName=master) [](https://spacy.io) [](https://github.com/ambv/black)  [](https://zenodo.org/badge/latestdoi/244012020)
spaCy pipeline object for extracting values that correspond to a named entity (e.g., birth dates, account numbers, or laboratory results)
## Installation and usage
Install the library.
```bash
pip install extractacy
```
Import library and spaCy.
```python
import spacy
from spacy.pipeline import EntityRuler
from extractacy.extract import ValueExtractor
```
Load spacy language model. Set up an EntityRuler for the example.
```python
nlp = spacy.load("en_core_web_sm")
# Set up entity ruler
ruler = nlp.add_pipe("entity_ruler")
patterns = [
{"label": "TEMP_READING", "pattern": [{"LOWER": "temperature"}]},
{"label": "TEMP_READING", "pattern": [{"LOWER": "temp"}]},
{
"label": "DISCHARGE_DATE",
"pattern": [{"LOWER": "discharge"}, {"LOWER": "date"}],
},
]
ruler.add_patterns(patterns)
```
Define which entities you would like to link patterns to. Each entity needs 3 things:
1) patterns to search for (list). This relies on [spaCy token matching syntax](https://spacy.io/usage/rule-based-matching#matcher).
2) n_tokens to search around a named entity (`int` or `sent`)
3) direction (`right`, `left`, `both`)
```python
# Define ent_patterns for value extraction
ent_patterns = {
"DISCHARGE_DATE": {"patterns": [[{"SHAPE": "dd/dd/dddd"}],[{"SHAPE": "dd/d/dddd"}]],"n": 2, "direction": "right"},
"TEMP_READING": {"patterns": [[
{"LIKE_NUM": True},
{"LOWER": {"IN": ["f", "c", "farenheit", "celcius", "centigrade", "degrees"]}
},
]
],
"n": "sent",
"direction": "both"
},
}
```
Add ValueExtractor to spaCy processing pipeline
```python
nlp.add_pipe("valext", config={"ent_patterns":ent_patterns}, last=True)
doc = nlp("Discharge Date: 11/15/2008. Patient had temp reading of 102.6 degrees.")
for e in doc.ents:
if e._.value_extract:
print(e.text, e.label_, e._.value_extract)
## Discharge Date DISCHARGE_DATE 11/15/2008
## temp reading TEMP_READING 102.6 degrees
```
## Contributing
[contributing](https://github.com/jenojp/negspacy/blob/master/CONTRIBUTING.md)
## Authors
* Jeno Pizarro
## License
[license](https://github.com/jenojp/extractacy/blob/master/LICENSE)