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Contextual Matcher

During feature extraction, it may be necessary to search for additional patterns in their neighborhood, namely:

  • patterns to discard irrelevant entities
  • patterns to enrich these entities and store some information

For example, to extract mentions of non-benign cancers, we need to discard all extractions that mention "benin" in their immediate neighborhood. Although such a filtering is feasible using a regular expression, it essentially requires modifying each of the regular expressions.

The ContextualMatcher allows to perform this extraction in a clear and concise way.

The configuration file

The whole ContextualMatcher pipeline component is basically defined as a list of pattern dictionaries. Let us see step by step how to build such a list using the example stated just above.

a. Finding mentions of cancer

To do this, we can build either a set of terms or a set of regex. terms will be used to search for exact matches in the text. While less flexible, it is faster than using regex. In our case we could use the following lists (which are of course absolutely not exhaustives):

terms = [
    "cancer",
    "tumeur",
]

regex = [
    "adeno(carcinom|[\s-]?k)",
    "neoplas",
    "melanom",
]

Maybe we want to exclude mentions of benign cancers:

benign = "benign|benin"

b. Find mention of a stage and extract its value

For this we will forge a RegEx with one capturing group (basically a pattern enclosed in parentheses):

stage = "stade (I{1,3}V?|[1234])"

This will extract stage between 1 and 4

We can add a second regex to try to capture if the cancer is in a metastasis stage or not:

metastase = "(metasta)"

c. The complete configuration

We can now put everything together:

cancer = dict(
    source="Cancer solide",
    regex=regex,
    terms=terms,
    regex_attr="NORM",
    exclude=dict(
        regex=benign,
        window=3,
    ),
    assign=[
        dict(
            name="stage",
            regex=stage,
            window=(-10, 10),
            replace_entity=False,
            reduce_mode=None,
        ),
        dict(
            name="metastase",
            regex=metastase,
            window=10,
            replace_entity=False,
            reduce_mode="keep_last",
        ),
    ],
)

Here the configuration consists of a single dictionary. We might want to also include lymphoma in the matcher:

lymphome = dict(
    source="Lymphome",
    regex=["lymphom", "lymphangio"],
    regex_attr="NORM",
    exclude=dict(
        regex=["hodgkin"],  # (1)
        window=3,
    ),
)
  1. We are excluding "Lymphome de Hodgkin" here

In this case, the configuration can be concatenated in a list:

patterns = [cancer, lymphome]

Available parameters for more flexibility

3 main parameters can be used to refine how entities will be formed

The include_assigned parameter

Following the previous example, you might want your extracted entities to include, if found, the cancer stage and the metastasis status. This can be achieved by setting include_assigned=True in the pipe configuration.

For instance, from the sentence "Le patient a un cancer au stade 3", the extracted entity will be:

  • "cancer" if include_assigned=False
  • "cancer au stade 3" if include_assigned=True

The reduce_mode parameter

It may happen that an assignment matches more than once. For instance, in the (nonsensical) sentence "Le patient a un cancer au stade 3 et au stade 4", both "stade 3" and "stade 4" will be matched by the stage assign key. Depending on your use case, you may want to keep all the extractions, or just one.

  • If reduce_mode=None (default), all extractions are kept in a list
  • If reduce_mode="keep_first", only the extraction closest to the main matched entity will be kept (in this case, it would be "stade 3" since it is the closest to "cancer")
  • If reduce_mode=="keep_last", only the furthest extraction is kept.

The replace_entity parameter

This parameter can be se to True only for a single assign key per dictionary. This limitation comes from the purpose of this parameter: If set to True, the corresponding assign key will be returned as the entity, instead of the match itself. For clarity, let's take the same sentence "Le patient a un cancer au stade 3" as an example:

  • if replace_entity=True in the stage assign key, then the extracted entity will be "stade 3" instead of "cancer"
  • if replace_entity=False for every assign key, the returned entity will be, as expected, "cancer"

Please notice that with replace_entity set to True, if the correponding assign key matches nothing, the entity will be discarded.

Examples

import edsnlp

nlp = edsnlp.blank("eds")

nlp.add_pipe("sentences")
nlp.add_pipe("normalizer")

nlp.add_pipe(
    "eds.contextual-matcher",
    name="Cancer",
    config=dict(
        patterns=patterns,
        label="cancer",
    ),
)

Let us see what we can get from this pipeline with a few examples

txt = "Le patient a eu un cancer il y a 5 ans"
doc = nlp(txt)
ent = doc.ents[0]

ent.label_
# Out: cancer

ent._.source
# Out: Cancer solide

ent.text, ent.start, ent.end
# Out: ('cancer', 5, 6)

Let us check that when a benign mention is present, the extraction is excluded:

txt = "Le patient a eu un cancer relativement bénin il y a 5 ans"
doc = nlp(txt)

doc.ents
# Out: ()

All informations extracted from the provided assign configuration can be found in the assigned attribute under the form of a dictionary:

txt = "Le patient a eu un cancer de stade 3."
doc = nlp(txt)

doc.ents[0]._.assigned
# Out: {'stage': '3'}

However, most of the configuration is provided in the patterns key, as a pattern dictionary or a list of pattern dictionaries

The pattern dictionary

Description

A patterr is a nested dictionary with the following keys:

A label describing the pattern

A single Regex or a list of Regexes

An attributes to overwrite the given attr when matching with Regexes.

A single term or a list of terms (for exact matches)

A dictionary (or list of dictionaries) to define exclusion rules. Exclusion rules are given as Regexes, and if a match is found in the surrounding context of an extraction, the extraction is removed. Each dictionary should have the following keys:

Size of the context to use (in number of words). You can provide the window as:

  • A positive integer, in this case the used context will be taken after the extraction
  • A negative integer, in this case the used context will be taken before the extraction
  • A tuple of integers (start, end), in this case the used context will be the snippet from start tokens before the extraction to end tokens after the extraction

A single Regex or a list of Regexes.

A dictionary to refine the extraction. Similarily to the exclude key, you can provide a dictionary to use on the context before and after the extraction.

A name (string)

Size of the context to use (in number of words). You can provide the window as:

  • A positive integer, in this case the used context will be taken after the extraction
  • A negative integer, in this case the used context will be taken before the extraction
  • A tuple of integers (start, end), in this case the used context will be the snippet from start tokens before the extraction to end tokens after the extraction

A dictionary where keys are labels and values are Regexes with a single capturing group

If set to True, the match from the corresponding assign key will be used as entity, instead of the main match. See this paragraph

Set how multiple assign matches are handled. See the documentation of the reduce_mode parameter

A full pattern dictionary example

dict(
    source="AVC",
    regex=[
        "accidents? vasculaires? cerebr",
    ],
    terms="avc",
    regex_attr="NORM",
    exclude=[
        dict(
            regex=["service"],
            window=3,
        ),
        dict(
            regex=[" a "],
            window=-2,
        ),
    ],
    assign=[
        dict(
            name="neo",
            regex=r"(neonatal)",
            expand_entity=True,
            window=3,
        ),
        dict(
            name="trans",
            regex="(transitoire)",
            expand_entity=True,
            window=3,
        ),
        dict(
            name="hemo",
            regex=r"(hemorragique)",
            expand_entity=True,
            window=3,
        ),
        dict(
            name="risk",
            regex=r"(risque)",
            expand_entity=False,
            window=-3,
        ),
    ],
)

Parameters

PARAMETER DESCRIPTION
patterns

The configuration dictionary

TYPE: Union[Dict[str, Any], List[Dict[str, Any]]]

assign_as_span

Whether to store eventual extractions defined via the assign key as Spans or as string

TYPE: bool DEFAULT: False

attr

Attribute to match on, eg TEXT, NORM, etc.

TYPE: str DEFAULT: NORM

ignore_excluded

Whether to skip excluded tokens during matching.

TYPE: bool DEFAULT: False

ignore_space_tokens

Whether to skip space tokens during matching.

TYPE: bool DEFAULT: False

alignment_mode

Overwrite alignment mode.

TYPE: str DEFAULT: expand

regex_flags

RegExp flags to use when matching, filtering and assigning (See here)

TYPE: Union[RegexFlag, int] DEFAULT: 0

include_assigned

Whether to include (eventual) assign matches to the final entity

TYPE: bool DEFAULT: False

label_name

Deprecated, use label instead. The label to assign to the matched entities

TYPE: Optional[str] DEFAULT: None

label

The label to assign to the matched entities

TYPE: str DEFAULT: None

span_setter

How to set matches on the doc

TYPE: SpanSetterArg DEFAULT: {'ents': True}

Authors and citation

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