spaCy

This package provides a stateful spaCy component to add iamsystem algorithm in a spaCy pipeline. Since a Matcher configuration is not JSON serializable, matcher’s parameters are passed in registered functions:

from typing import Iterable
from typing import List

import spacy

from spacy.lang.fr import French

from iamsystem import Abbreviations
from iamsystem import Entity
from iamsystem import FuzzyAlgo
from iamsystem import IKeyword
from iamsystem import IStopwords
from iamsystem import Terminology
from iamsystem import french_tokenizer
from iamsystem.spacy import IAMsystemSpacy  # noqa
from iamsystem.spacy import IsStopSpacy
from iamsystem.spacy import TokenSpacyAdapter

@spacy.registry.misc("umls_ents.v1")
def get_termino_umls() -> Iterable[IKeyword]:
    """An imaginary set of umls ents."""
    termino = Terminology()
    ent1 = Entity("Insuffisance Cardiaque", "I50.9")
    ent2 = Entity("Insuffisance Cardiaque Gauche", "I50.1")
    termino.add_keywords(keywords=[ent1, ent2])
    return termino

@spacy.registry.misc("fuzzy_algos_short_notes.v1")
def get_fuzzy_algos_short_notes() -> List[FuzzyAlgo]:
    """An imaginary set of fuzzy algorithms for medical short notes."""
    tokenizer = french_tokenizer()
    abbs = Abbreviations(name="French medical abbreviations")
    abbs.add(
        short_form="ins", long_form="insuffisance", tokenizer=tokenizer
    )
    abbs.add(
        short_form="ic",
        long_form="insuffisance cardiaque",
        tokenizer=tokenizer,
    )
    return [abbs]

@spacy.registry.misc("stopwords_spacy.v1")
def get_stopwords_short_notes() -> IStopwords[TokenSpacyAdapter]:
    """Use spaCy stopword list."""
    stopwords = IsStopSpacy()
    return stopwords

nlp = French()
nlp.add_pipe(
    "iamsystem",
    name="iamsystem",
    last=True,
    config={
        "keywords": {"@misc": "umls_ents.v1"},
        "stopwords": {"@misc": "stopwords_spacy.v1"},
        "fuzzy_algos": {"@misc": "fuzzy_algos_short_notes.v1"},
        "w": 1,
        "remove_nested_annots": True,
    },
)
doc = nlp("ic gauche")
self.assertEqual(1, len(doc.spans["iamsystem"]))
spans = doc.spans["iamsystem"]
for span in spans:
    print(span._.iamsystem)
# ic gauche	0 9	Insuffisance Cardiaque Gauche (I50.1)

See IAMsystemSpacy to configure this component.