medkit.text.spacy.spacy_utils

Functions:

build_spacy_doc_from_medkit_doc(nlp, medkit_doc)	Create a Spacy Doc from a TextDocument.
build_spacy_doc_from_medkit_segment(nlp, segment)	Create a Spacy Doc from a Segment.
extract_anns_and_attrs_from_spacy_doc(spacy_doc)	Given a spacy document, convert selected entities or spans into Segments.

extract_anns_and_attrs_from_spacy_doc(spacy_doc, medkit_source_ann=None, entities=None, span_groups=None, attrs=None, attribute_factories=None, rebuild_medkit_anns_and_attrs=False)

Given a spacy document, convert selected entities or spans into Segments. Extract attributes for each annotation in the document.

Parameters:

• spacy_doc (Doc) – A Spacy Doc with spans to be converted

• medkit_source_ann (Segment, optional) – Segment used to rebuild spans referencing the original text

• entities (list of str, optional) – Labels of entities to be extracted If None (default) all new entities will be extracted as annotations

• span_groups (list of str, optional) – Name of span groups to be extracted If None (default) all new spans will be extracted as annotations

• attrs (list of str, optional) – Name of custom attributes to extract from the annotations that will be included. If None (default) all the custom attributes will be extracted

• attribute_factories (dict of str to Callable, optional) – Mapping of factories in charge of converting spacy attributes to medkit attributes. Factories will receive a spacy span and an attribute label when called. The key in the mapping is the attribute label.

• rebuild_medkit_anns_and_attrs (bool, default=False) – If True the annotations and attributes with medkit ids will become new annotations/attributes with new ids. If False (default) the annotations and attributes with medkit ids are not rebuilt, only new annotations and attributes are returned

Return type:

tuple[list[Segment], dict[str, list[Attribute]]]

Returns:

• annotations (list of Segment) – Segments extracted from the spacy Doc object

• attributes_by_ann (dict of str to list of Attribute) – Attributes extracted for each annotation, the key is a medkit uid

Raises:

ValueError – Raises when the given medkit source and the spacy doc do not have the same medkit uid

build_spacy_doc_from_medkit_doc(nlp, medkit_doc, labels_anns=None, attrs=None, include_medkit_info=True)

Create a Spacy Doc from a TextDocument.

Parameters:

• nlp (Language) – Language object with the loaded pipeline from Spacy

• medkit_doc (TextDocument) – TextDocument to convert

• labels_anns (list[str] | None) – Labels of annotations to include in the spacy document. If None (default) all the annotations will be included.

• attrs (list[str] | None) – Labels of attributes to add in the annotations that will be included. If None (default) all the attributes will be added as custom attributes in each annotation included.

• include_medkit_info (bool) – If True, medkitID is included as an extension in the Doc object to identify the medkit source annotation. If False, no information about IDs is included

Return type:

Doc

Returns:

Doc – A Spacy Doc with the selected annotations included.

build_spacy_doc_from_medkit_segment(nlp, segment, annotations=None, attrs=None, include_medkit_info=True)

Create a Spacy Doc from a Segment.

Parameters:

• nlp (Language) – Language object with the loaded pipeline from Spacy

• segment (Segment) – Segment to convert, this annotation contains the text to create the spacy doc

• annotations (list[Segment] | None) – List of annotations in segment to include

• attrs (list[str] | None) – Labels of attributes to add in the annotations that will be included. If None (default) all the attributes will be added as custom attributes in each annotation included.

• include_medkit_info (bool) – If True, medkitID is included as an extension in the Doc object to identify the medkit source annotation. If False, no information about IDs is included.

Return type:

Doc

Returns:

Doc – A Spacy Doc with the selected annotations included.