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"],  # Every key in the terms dictionary is mapped to a concept.

    patient="patient",  # The eds.matcher pipeline expects a list of expressions, or a single expression.

)

regex = dict(
    covid=r"coronavirus|covid[-\s]?19|sars[-\s]cov[-\s]2",  # We can also define regular expression patterns.

)

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

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.