Matcher

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

Examples

Let us redefine the pipeline :

import edsnlp

nlp = edsnlp.blank("eds")

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

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

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.

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

Parameters

PARAMETER	DESCRIPTION
nlp	The pipeline object. TYPE: PipelineProtocol
name	The name of the component. TYPE: Optional[str] DEFAULT: 'eds.matcher'
terms	A dictionary of terms. TYPE: Optional[Patterns] DEFAULT: None
regex	A dictionary of regular expressions. TYPE: Optional[Patterns] DEFAULT: None
attr	The default attribute to use for matching. Can be overridden using the terms and regex configurations. TYPE: str DEFAULT: TEXT
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: Literal['exact', 'simstring'] DEFAULT: exact
term_matcher_config	Parameters of the matcher class TYPE: Dict[str, Any] DEFAULT: {}
span_setter	How to set the spans in the doc. TYPE: SpanSetterArg DEFAULT: {'ents': True}

Authors and citation

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

---