Normalisation

The normalisation scheme used by EDS-NLP adheres to the non-destructive doctrine. In other words,

nlp(text).text == text

is always true.

To achieve this, the input text is never modified. Instead, our normalisation strategy focuses on two axes:

1. Only the NORM and tag_ attributes are modified by the normalizer pipeline component ;
2. Pipes (e.g., pollution) can mark tokens as excluded by setting the extension Token.tag_ to EXCLUDED or as space by setting the extension Token.tag_ to SPACE. It enables downstream matchers to skip excluded tokens.

The normaliser can act on the input text in five dimensions :

1. Move the text to lowercase.
2. Remove accents. We use a deterministic approach to avoid modifying the character-length of the text, which helps for RegEx matching.
3. Normalize apostrophes and quotation marks, which are often coded using special characters.
4. Detect spaces and new lines and mark them as such (to be skipped later)
5. Detect tokens in pollutions patterns and mark them as such (to be skipped later)

Usage

The normalisation is handled by the single eds.normalizer pipeline component. The following code snippet is complete, and should run as is.

import edsnlp
from edsnlp.matchers.utils import get_text

nlp = edsnlp.blank("eds")
nlp.add_pipe("eds.normalizer")

# Notice the special character used for the apostrophe and the quotes
text = "Le patient est admis à l'hôpital le 23 août 2021 pour une douleur ʺaffreuse” à l`estomac."

doc = nlp(text)

get_text(doc, attr="NORM", ignore_excluded=False)
# Out: le patient est admis a l'hopital le 23 aout 2021 pour une douleur "affreuse" a l'estomac.

Utilities

To simplify the use of the normalisation output, we provide the get_text utility function. It computes the textual representation for a Span or Doc object.

Moreover, every span exposes a normalized_variant extension getter, which computes the normalised representation of an entity on the fly.

Configuration

The pipeline component can be configured using the following parameters :

Pipes

Let's review each subcomponent.

Lowercase

The eds.lowercase pipeline component transforms every token to lowercase. It is not configurable.

Consider the following example :

import edsnlp
from edsnlp.matchers.utils import get_text

config = dict(
    lowercase=True,
    accents=False,
    quotes=False,
    spaces=False,
    pollution=False,
)

nlp = edsnlp.blank("eds")
nlp.add_pipe("eds.normalizer", config=config)

text = "Pneumopathie à NBNbWbWbNbWbNBNbNbWbW `coronavirus'"

doc = nlp(text)

get_text(doc, attr="NORM", ignore_excluded=False)
# Out: pneumopathie à nbnbwbwbnbwbnbnbnbwbw 'coronavirus'

Accents

The eds.accents pipeline component removes accents. To avoid edge cases, the component uses a specified list of accentuated characters and their unaccented representation, making it more predictable than using a library such as unidecode.

Consider the following example :

import edsnlp
from edsnlp.matchers.utils import get_text

config = dict(
    lowercase=False,
    accents=True,
    quotes=False,
    spaces=False,
    pollution=False,
)

nlp = edsnlp.blank("eds")
nlp.add_pipe("eds.normalizer", config=config)

text = "Pneumopathie à NBNbWbWbNbWbNBNbNbWbW `coronavirus'"

doc = nlp(text)

get_text(doc, attr="NORM", ignore_excluded=False)
# Out: Pneumopathie a NBNbWbWbNbWbNBNbNbWbW `coronavirus'

Apostrophes and quotation marks

Apostrophes and quotation marks can be encoded using unpredictable special characters. The eds.quotes component transforms every such special character to ' and ", respectively.

Consider the following example :

import edsnlp
from edsnlp.matchers.utils import get_text

config = dict(
    lowercase=False,
    accents=False,
    quotes=True,
    spaces=False,
    pollution=False,
)

nlp = edsnlp.blank("eds")
nlp.add_pipe("eds.normalizer", config=config)

text = "Pneumopathie à NBNbWbWbNbWbNBNbNbWbW `coronavirus'"

doc = nlp(text)

get_text(doc, attr="NORM", ignore_excluded=False)
# Out: Pneumopathie à NBNbWbWbNbWbNBNbNbWbW 'coronavirus'

Spaces

This is not truly a normalisation component, but this allows us to detect spaces tokens ahead of the other components and encode it as using the tag_ attribute for fast matching.

import edsnlp

config = dict(
    lowercase=False,
    accents=False,
    quotes=False,
    spaces=True,
    pollution=False,
)

nlp = edsnlp.blank("eds")
nlp.add_pipe("eds.normalizer", config=config)

doc = nlp("Phrase    avec des espaces \n et un retour à la ligne")
[t.tag_ for t in doc]
# Out: ['', 'SPACE', '', '', '', 'SPACE', '', '', '', '', '', '']

Pollution

The pollution pipeline component uses a set of regular expressions to detect pollutions (irrelevant non-medical text that hinders text processing). Corresponding tokens are marked as excluded (by setting Token._.excluded to True), enabling the use of the phrase matcher.

Consider the following example :

import edsnlp
from edsnlp.matchers.utils import get_text

config = dict(
    lowercase=False,
    accents=True,
    quotes=False,
    spaces=False,
    pollution=True,
)

nlp = edsnlp.blank("eds")
nlp.add_pipe("eds.normalizer", config=config)

text = "Pneumopathie à NBNbWbWbNbWbNBNbNbWbW `coronavirus'"

doc = nlp(text)

get_text(doc, attr="NORM", ignore_excluded=False)
# Out: Pneumopathie a NBNbWbWbNbWbNBNbNbWbW `coronavirus'

get_text(doc, attr="TEXT", ignore_excluded=True)
# Out: Pneumopathie à `coronavirus'

This example above shows that the normalisation scheme works on two axes: non-destructive text modification and exclusion of tokens. The two are independent: a matcher can use the NORM attribute but keep excluded tokens, and conversely, match on TEXT while ignoring excluded tokens.

Types of pollution

Pollution can come in various forms in clinical texts. We provide a small set of possible pollutions patterns that can be enabled or disabled as needed.

For instance, if we consider biology tables as pollution, we only need to instantiate the normalizer pipe as follows:

import edsnlp

nlp = edsnlp.blank("eds")
nlp.add_pipe(
    "eds.normalizer",
    config=dict(
        pollution=dict(
            biology=True,
        ),
    ),
)

Custom pollution

If you want to exclude specific patterns, you can provide them as a RegEx (or a list of Regexes). For instance, to consider text between "AAA" and "ZZZ" as pollution you might use:

import edsnlp

nlp = edsnlp.blank("eds")
nlp.add_pipe(
    "eds.normalizer",
    config=dict(
        pollution=dict(
            custom_pollution=r"AAA.*ZZZ",
        ),
    ),
)

Authors and citation

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