extractacy - pattern extraction and named entity linking for spaCy
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.
pip install extractacyImport library and spaCy.
import spacy
from spacy.pipeline import EntityRuler
from extractacy.extract import ValueExtractorLoad spacy language model. Set up an EntityRuler for the example.
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:
- patterns to search for (list). This relies on spaCy token matching syntax.
- n_tokens to search around a named entity (
intorsent) - direction (
right,left,both)
# 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
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 degreesContributing
Authors
- Jeno Pizarro