Matcher

EDS-NLP simplifies the matching process by exposing a eds.matcher pipeline that can match on terms or regular expressions.

Usage

Let us redefine the pipeline :

import spacy

nlp = spacy.blank("fr")

terms = dict(
    covid=["coronavirus", "covid19"],  # (1)
    patient="patient",  # (2)
)

regex = dict(
    covid=r"coronavirus|covid[-\s]?19|sars[-\s]cov[-\s]2",  # (3)
)

nlp.add_pipe(
    "eds.matcher",
    config=dict(
        terms=terms,
        regex=regex,
        attr="LOWER",
        term_matcher="exact",
        term_matcher_config={},
    ),
)

1. Every key in the terms dictionary is mapped to a concept.
2. The eds.matcher pipeline expects a list of expressions, or a single expression.
3. We can also define regular expression patterns.

This snippet is complete, and should run as is.

Configuration

The pipeline can be configured using the following parameters :

Parameter	Explanation	Default
terms	Terms patterns. Expects a dictionary.	None (use regex only)
regex	RegExp patterns. Expects a dictionary.	None (use terms only)
term_matcher	Which algorithm should we use : exact or simstring	"LOWER"
term_matcher_config	Config of the algorithm (SimstringMatcher's for simstring)	"LOWER"
attr	spaCy attribute to match on (eg NORM, LOWER)	"TEXT"
ignore_excluded	Whether to skip excluded tokens during matching	False

Patterns, be they terms or regex, are defined as dictionaries where keys become the label of the extracted entities. Dictionary values are a either a single expression or a list of expressions that match the concept (see example).

Authors and citation

The eds.matcher pipeline was developed by AP-HP's Data Science team.