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	DESCRIPTION
`terms`	A dictionary of terms. TYPE: `Optional[Patterns]` DEFAULT: `None`
`regex`	A dictionary of regular expressions. TYPE: `Optional[Patterns]` DEFAULT: `'TEXT'`
`attr`	The default attribute to use for matching. Can be overridden using the `terms` and `regex` configurations. TYPE: `str` DEFAULT: `None`
`ignore_excluded`	Whether to skip excluded tokens (requires an upstream pipeline to mark excluded tokens). TYPE: `bool` DEFAULT: `False`
`ignore_space_tokens`	Whether to skip space tokens during matching. You won't be able to match on newlines if this is enabled and the "spaces"/"newline" option of `eds.normalizer` is enabled (by default). TYPE: `bool` DEFAULT: `False`
`term_matcher`	The matcher to use for matching phrases ? One of (exact, simstring) TYPE: `GenericTermMatcher` DEFAULT: `GenericTermMatcher.exact`
`term_matcher_config`	Parameters of the matcher class TYPE: `Dict[str, Any]` DEFAULT: `{}`

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.