medkit.core.text#

APIs#

For accessing these APIs, you may use import like this:

from medkit.core.text import <api_to_import>

Classes:

`AnySpan`()
`ContextOperation`([uid, name])	Abstract operation for context detection.
`CustomTextOpType`(value)	Supported function types for creating custom text operations.
`Entity`(label, text, spans[, attrs, ...])	Text entity referencing part of an `TextDocument`.
`EntityAttributeContainer`(owner_id)	Manage a list of attributes attached to a text entity.
`EntityNormAttribute`(kb_name, kb_id[, ...])	Normalization attribute linking an entity to an ID in a knowledge base
`ModifiedSpan`(length, replaced_spans)	Slice of text not present in the original text
`NEROperation`([uid, name])	Abstract operation for detecting entities.
`Relation`(label, source_id, target_id[, ...])	Relation between two text entities.
`Segment`(label, text, spans[, attrs, ...])	Text segment referencing part of an `TextDocument`.
`SegmentationOperation`([uid, name])	Abstract operation for segmenting text.
`Span`(start, end)	Slice of text extracted from the original text
`TextAnnotation`(label[, attrs, metadata, ...])	Base abstract class for all text annotations
`TextAnnotationContainer`(doc_id, raw_segment)	Manage a list of text annotations belonging to a text document.
`TextDocument`(text[, anns, attrs, metadata, uid])	Document holding text annotations
`UMLSNormAttribute`(cui, umls_version[, term, ...])	Normalization attribute linking an entity to a CUI in the UMLS knowledge base

Functions:

create_text_operation(function, function_type)

Function for instantiating a custom test operation from a user-defined function

class TextAnnotation(label, attrs=None, metadata=None, uid=None, attr_container_class=<class 'AttributeContainer'>)[source]#

Base abstract class for all text annotations

Variables

uid (str) – Unique identifier of the annotation.
label (str) – The label for this annotation (e.g., SENTENCE)
attrs (medkit.core.attribute_container.AttributeContainer) – Attributes of the annotation. Stored in a :class:{~medkit.core.AttributeContainer} but can be passed as a list at init.
metadata (Dict[str, Any]) – The metadata of the annotation
keys (Set[str]) – Pipeline output keys to which the annotation belongs to.

Methods:

get_subclass_for_data_dict(data_dict)

Return the subclass that corresponds to the class name found in a data dict

classmethod get_subclass_for_data_dict(data_dict)#

Return the subclass that corresponds to the class name found in a data dict

Parameters: data_dict (Dict[str, Any]) – Data dict returned by the to_dict() method of a subclass (or of the base class itself)
Return type: Optional[Type[Self]]
Returns: subclass – Subclass that generated data_dict, or None if data_dict correspond to the base class itself.

class Segment(label, text, spans, attrs=None, metadata=None, uid=None, store=None, attr_container_class=<class 'AttributeContainer'>)[source]#

Text segment referencing part of an TextDocument.

Variables

uid (str) – The segment identifier.
label (str) – The label for this segment (e.g., SENTENCE)
text (str) – Text of the segment.
spans (List[medkit.core.text.span.AnySpan]) – List of spans indicating which parts of the segment text correspond to which part of the document’s full text.
attrs (medkit.core.attribute_container.AttributeContainer) – Attributes of the segment. Stored in a :class:{~medkit.core.AttributeContainer} but can be passed as a list at init.
metadata (Dict[str, Any]) – The metadata of the segment
keys (Set[str]) – Pipeline output keys to which the segment belongs to.

Methods:

`from_dict`(segment_dict)	Creates a Segment from a dict
`get_subclass_for_data_dict`(data_dict)	Return the subclass that corresponds to the class name found in a data dict

classmethod from_dict(segment_dict)[source]#

Creates a Segment from a dict

Parameters: segment_dict (dict) – A dictionary from a serialized segment as generated by to_dict()
Return type: Self

classmethod get_subclass_for_data_dict(data_dict)#

Return the subclass that corresponds to the class name found in a data dict

Parameters: data_dict (Dict[str, Any]) – Data dict returned by the to_dict() method of a subclass (or of the base class itself)
Return type: Optional[Type[Self]]
Returns: subclass – Subclass that generated data_dict, or None if data_dict correspond to the base class itself.

class Entity(label, text, spans, attrs=None, metadata=None, uid=None, store=None, attr_container_class=<class 'EntityAttributeContainer'>)[source]#

Text entity referencing part of an TextDocument.

Variables

uid (str) – The entity identifier.
label (str) – The label for this entity (e.g., DISEASE)
text (str) – Text of the entity.
spans (List[medkit.core.text.span.AnySpan]) – List of spans indicating which parts of the entity text correspond to which part of the document’s full text.
attrs (medkit.core.text.entity_attribute_container.EntityAttributeContainer) – Attributes of the entity. Stored in a :class:{~medkit.core.EntityAttributeContainer} but can be passed as a list at init.
metadata (Dict[str, Any]) – The metadata of the entity
keys (Set[str]) – Pipeline output keys to which the entity belongs to.

Methods:

`from_dict`(segment_dict)	Creates a Segment from a dict
`get_subclass_for_data_dict`(data_dict)	Return the subclass that corresponds to the class name found in a data dict

classmethod from_dict(segment_dict)#

Creates a Segment from a dict

Parameters: segment_dict (dict) – A dictionary from a serialized segment as generated by to_dict()
Return type: Self

classmethod get_subclass_for_data_dict(data_dict)#

Return the subclass that corresponds to the class name found in a data dict

Parameters: data_dict (Dict[str, Any]) – Data dict returned by the to_dict() method of a subclass (or of the base class itself)
Return type: Optional[Type[Self]]
Returns: subclass – Subclass that generated data_dict, or None if data_dict correspond to the base class itself.

class Relation(label, source_id, target_id, attrs=None, metadata=None, uid=None, store=None, attr_container_class=<class 'AttributeContainer'>)[source]#

Relation between two text entities.

Variables

uid (str) – The identifier of the relation
label (str) – The relation label
source_id (str) – The identifier of the entity from which the relation is defined
target_id (str) – The identifier of the entity to which the relation is defined
attrs (medkit.core.attribute_container.AttributeContainer) – The attributes of the relation
metadata (Dict[str, Any]) – The metadata of the relation
keys (Set[str]) – Pipeline output keys to which the relation belongs to

Methods:

`from_dict`(relation_dict)	Creates a Relation from a dict
`get_subclass_for_data_dict`(data_dict)	Return the subclass that corresponds to the class name found in a data dict

classmethod get_subclass_for_data_dict(data_dict)#

Return the subclass that corresponds to the class name found in a data dict

Parameters: data_dict (Dict[str, Any]) – Data dict returned by the to_dict() method of a subclass (or of the base class itself)
Return type: Optional[Type[Self]]
Returns: subclass – Subclass that generated data_dict, or None if data_dict correspond to the base class itself.

classmethod from_dict(relation_dict)[source]#

Creates a Relation from a dict

Parameters: relation_dict (dict) – A dictionary from a serialized relation as generated by to_dict()
Return type: Self

class TextAnnotationContainer(doc_id, raw_segment)[source]#

Manage a list of text annotations belonging to a text document.

This behaves more or less like a list: calling len() and iterating are supported. Additional filtering is available through the get() method.

Also provides retrieval of entities, segments, relations, and handling of raw segment.

Instantiate the annotation container

Parameters: doc_id (str) – The identifier of the document which annotations belong to.

Attributes:

`entities`	Return the list of entities
`relations`	Return the list of relations
`segments`	Return the list of segments

Methods:

`add`(ann)	Attach an annotation to the document.
`get`(*[, label, key])	Return a list of the annotations of the document, optionally filtering by label or key.
`get_by_id`(uid)	Return the annotation corresponding to a specific identifier.
`get_entities`(*[, label, key])	Return a list of the entities of the document, optionally filtering by label or key.
`get_ids`(*[, label, key])	Return an iterator of the identifiers of the annotations of the document, optionally filtering by label or key.
`get_relations`(*[, label, key, source_id])	Return a list of the relations of the document, optionally filtering by label, key or source entity.
`get_segments`(*[, label, key])	Return a list of the segments of the document (not including entities), optionally filtering by label or key.

property segments: List[medkit.core.text.annotation.Segment]#

Return the list of segments

Return type: List[Segment]

property entities: List[medkit.core.text.annotation.Entity]#

Return the list of entities

Return type: List[Entity]

property relations: List[medkit.core.text.annotation.Relation]#

Return the list of relations

Return type: List[Relation]

add(ann)[source]#

Attach an annotation to the document.

Parameters: ann (TextAnnotation) – Annotation to add.
Raises: ValueError – If the annotation is already attached to the document (based on annotation.uid)

get(*, label=None, key=None)[source]#

Return a list of the annotations of the document, optionally filtering by label or key.

Parameters

label (Optional[str]) – Label to use to filter annotations.
key (Optional[str]) – Key to use to filter annotations.

Return type

List[TextAnnotation]

get_by_id(uid)[source]#

Return the annotation corresponding to a specific identifier.

Parameters: uid – Identifier of the annotation to return.
Return type: TextAnnotation

get_segments(*, label=None, key=None)[source]#

Return a list of the segments of the document (not including entities), optionally filtering by label or key.

Parameters

label (Optional[str]) – Label to use to filter segments.
key (Optional[str]) – Key to use to filter segments.

Return type

List[Segment]

get_entities(*, label=None, key=None)[source]#

Return a list of the entities of the document, optionally filtering by label or key.

Parameters

label (Optional[str]) – Label to use to filter entities.
key (Optional[str]) – Key to use to filter entities.

Return type

List[Entity]

get_relations(*, label=None, key=None, source_id=None)[source]#

Return a list of the relations of the document, optionally filtering by label, key or source entity.

Parameters

label (Optional[str]) – Label to use to filter relations.
key (Optional[str]) – Key to use to filter relations.
source_id (Optional[str]) – Identifier of the source entity to use to filter relations.

Return type

List[Relation]

get_ids(*, label=None, key=None)#

Return an iterator of the identifiers of the annotations of the document, optionally filtering by label or key.

This method is provided, so it is easier to implement additional filtering in subclasses.

Parameters

label (Optional[str]) – Label to use to filter annotations.
key (Optional[str]) – Key to use to filter annotations.

Return type

Iterator[str]

class TextDocument(text, anns=None, attrs=None, metadata=None, uid=None)[source]#

Document holding text annotations

Annotations must be subclasses of TextAnnotation.

Variables

uid (str) – Unique identifier of the document.
text – Full document text.
anns (medkit.core.text.annotation_container.TextAnnotationContainer) – Annotations of the document. Stored in an TextAnnotationContainer but can be passed as a list at init.
attrs (medkit.core.attribute_container.AttributeContainer) – Attributes of the document. Stored in an AttributeContainer but can be passed as a list at init
metadata (Dict[str, Any]) – Document metadata.
raw_segment (medkit.core.text.annotation.Segment) –
Auto-generated segment containing the full unprocessed document text. To get the raw text as an annotation to pass to processing operations:
```
>>> doc = TextDocument(text="hello")
>>> raw_text = doc.anns.get(label=TextDocument.RAW_LABEL)[0]
```

Methods:

`from_dict`(doc_dict)	Creates a TextDocument from a dict
`from_dir`(path[, pattern, encoding])	Create documents from text files in a directory
`from_file`(path[, encoding])	Create a document from a text file
`get_snippet`(segment, max_extend_length)	Return a portion of the original text containing the annotation
`get_subclass_for_data_dict`(data_dict)	Return the subclass that corresponds to the class name found in a data dict

classmethod get_subclass_for_data_dict(data_dict)#

Return the subclass that corresponds to the class name found in a data dict

Parameters: data_dict (Dict[str, Any]) – Data dict returned by the to_dict() method of a subclass (or of the base class itself)
Return type: Optional[Type[Self]]
Returns: subclass – Subclass that generated data_dict, or None if data_dict correspond to the base class itself.

classmethod from_dict(doc_dict)[source]#

Creates a TextDocument from a dict

Parameters: doc_dict (dict) – A dictionary from a serialized TextDocument as generated by to_dict()
Return type: Self

classmethod from_file(path, encoding='utf-8')[source]#

Create a document from a text file

Parameters

path (PathLike) – Path of the text file
encoding (Optional[str]) – Text encoding to use

Return type

Self

Returns

TextDocument – Text document with contents of path as text. The file path is included in the document metadata.

classmethod from_dir(path, pattern='*.txt', encoding='utf-8')[source]#

Create documents from text files in a directory

Parameters

path (PathLike) – Path of the directory containing text files
pattern (str) – Glob pattern to match text files in path
encoding (Optional[str]) – Text encoding to use

Return type

List[Self]

Returns

List[TextDocument] – Text documents with contents of each file as text

get_snippet(segment, max_extend_length)[source]#

Return a portion of the original text containing the annotation

Parameters

segment (Segment) – The annotation
max_extend_length (int) – Maximum number of characters to use around the annotation

Return type

str

Returns

str – A portion of the text around the annotation

class EntityAttributeContainer(owner_id)[source]#

Manage a list of attributes attached to a text entity.

This behaves more or less like a list: calling len() and iterating are supported. Additional filtering is available through the get() method.

Also provides retrieval of normalization attributes.

Attributes:

norms

Return the list of normalization attributes

Methods:

`add`(attr)	Attach an attribute to the annotation.
`get`(*[, label])	Return a list of the attributes of the annotation, optionally filtering by label.
`get_by_id`(uid)	Return the attribute corresponding to a specific identifier.
`get_norms`()	Return a list of the normalization attributes of the annotation

property norms: List[medkit.core.text.entity_norm_attribute.EntityNormAttribute]#

Return the list of normalization attributes

Return type: List[EntityNormAttribute]

add(attr)[source]#

Attach an attribute to the annotation.

Parameters: attr (Attribute) – Attribute to add.
Raises: ValueError – If the attribute is already attached to the annotation (based on attr.uid).

get_norms()[source]#

Return a list of the normalization attributes of the annotation

Return type: List[EntityNormAttribute]

get(*, label=None)#

Return a list of the attributes of the annotation, optionally filtering by label.

Parameters: label (Optional[str]) – Label to use to filter attributes.
Return type: List[Attribute]

get_by_id(uid)#

Return the attribute corresponding to a specific identifier.

Parameters: uid (str) – Identifier of the attribute to return.
Return type: Attribute

class EntityNormAttribute(kb_name, kb_id, kb_version=None, term=None, score=None, metadata=None, uid=None)[source]#

Normalization attribute linking an entity to an ID in a knowledge base

Variables

uid (str) – Identifier of the attribute
label (str) – The attribute label, always set to EntityNormAttribute.LABEL
value (Optional[Any]) – String representation of the normalization, containing kb_id, along with kb_name if available (ex: “umls:C0011849”). For special cases where only term is available, it is used as value.
kb_name (Optional[str]) – Name of the knowledge base (ex: “icd”). Should always be provided except in special cases when we just want to store a normalized term.
kb_id (Optional[Any]) – ID in the knowledge base to which the annotation should be linked. Should always be provided except in special cases when we just want to store a normalized term.
kb_version (Optional[str]) – Optional version of the knowledge base.
term (Optional[str]) – Optional normalized version of the entity text.
score (Optional[float]) – Optional score reflecting confidence of this link.
metadata (Dict[str, Any]) – Metadata of the attribute

Attributes:

LABEL

Label used for all normalization attributes

Methods:

`copy`()	Create a new attribute that is a copy of the current instance, but with a new identifier
`from_dict`(data_dict)	Creates an Attribute from a dict
`get_subclass_for_data_dict`(data_dict)	Return the subclass that corresponds to the class name found in a data dict
`to_brat`()	Return a value compatible with the brat format
`to_spacy`()	Return a value compatible with spaCy

LABEL: ClassVar[str] = 'NORMALIZATION'#: Label used for all normalization attributes

copy()#

Create a new attribute that is a copy of the current instance, but with a new identifier

This is used when we want to duplicate an existing attribute onto a different annotation.

Return type: Attribute

classmethod get_subclass_for_data_dict(data_dict)#

Return the subclass that corresponds to the class name found in a data dict

Parameters: data_dict (Dict[str, Any]) – Data dict returned by the to_dict() method of a subclass (or of the base class itself)
Return type: Optional[Type[Self]]
Returns: subclass – Subclass that generated data_dict, or None if data_dict correspond to the base class itself.

to_brat()[source]#

Return a value compatible with the brat format

Return type: str

to_spacy()[source]#

Return a value compatible with spaCy

Return type: str

classmethod from_dict(data_dict)[source]#

Creates an Attribute from a dict

Parameters: attribute_dict (dict) – A dictionary from a serialized Attribute as generated by to_dict()
Return type: Self

class ContextOperation(uid=None, name=None, **kwargs)[source]#

Abstract operation for context detection. It uses a list of segments as input for running the operation and creates attributes that are directly appended to these segments.

Common initialization for all annotators:

assigning identifier to operation
storing class name, name and config in description

Parameters

uid (str) – Operation identifier
name – Operation name (defaults to class name)
kwargs – All other arguments of the child init useful to describe the operation

Examples

In the __init__ function of your annotator, use:

>>> init_args = locals()
>>> init_args.pop('self')
>>> super().__init__(**init_args)

Attributes:

description

Contains all the operation init parameters.

Methods:

set_prov_tracer(prov_tracer)

Enable provenance tracing.

property description: medkit.core.operation_desc.OperationDescription#

Contains all the operation init parameters.

Return type: OperationDescription

set_prov_tracer(prov_tracer)#

Enable provenance tracing.

Parameters: prov_tracer (ProvTracer) – The provenance tracer used to trace the provenance.

class NEROperation(uid=None, name=None, **kwargs)[source]#

Abstract operation for detecting entities. It uses a list of segments as input and produces a list of detected entities.

Common initialization for all annotators:

assigning identifier to operation
storing class name, name and config in description

Parameters

uid (str) – Operation identifier
name – Operation name (defaults to class name)
kwargs – All other arguments of the child init useful to describe the operation

Examples

In the __init__ function of your annotator, use:

>>> init_args = locals()
>>> init_args.pop('self')
>>> super().__init__(**init_args)

Attributes:

description

Contains all the operation init parameters.

Methods:

set_prov_tracer(prov_tracer)

Enable provenance tracing.

property description: medkit.core.operation_desc.OperationDescription#

Contains all the operation init parameters.

Return type: OperationDescription

set_prov_tracer(prov_tracer)#

Enable provenance tracing.

Parameters: prov_tracer (ProvTracer) – The provenance tracer used to trace the provenance.

class SegmentationOperation(uid=None, name=None, **kwargs)[source]#

Abstract operation for segmenting text. It uses a list of segments as input and produces a list of new segments.

Common initialization for all annotators:

assigning identifier to operation
storing class name, name and config in description

Parameters

uid (str) – Operation identifier
name – Operation name (defaults to class name)
kwargs – All other arguments of the child init useful to describe the operation

Examples

In the __init__ function of your annotator, use:

>>> init_args = locals()
>>> init_args.pop('self')
>>> super().__init__(**init_args)

Attributes:

description

Contains all the operation init parameters.

Methods:

set_prov_tracer(prov_tracer)

Enable provenance tracing.

property description: medkit.core.operation_desc.OperationDescription#

Contains all the operation init parameters.

Return type: OperationDescription

set_prov_tracer(prov_tracer)#

Enable provenance tracing.

Parameters: prov_tracer (ProvTracer) – The provenance tracer used to trace the provenance.

class CustomTextOpType(value)[source]#

Supported function types for creating custom text operations.

Attributes:

`CREATE_ONE_TO_N`	Take 1 data item, return N new data items.
`EXTRACT_ONE_TO_N`	Take 1 data item, return N existing data items
`FILTER`	Take 1 data item, return True or False.

CREATE_ONE_TO_N = 1#: Take 1 data item, return N new data items.

EXTRACT_ONE_TO_N = 2#: Take 1 data item, return N existing data items

FILTER = 3#: Take 1 data item, return True or False.

create_text_operation(function, function_type, name=None, args=None)[source]#

Function for instantiating a custom test operation from a user-defined function

Parameters

function (Callable) – User-defined function
function_type (CustomTextOpType) – Type of function. Supported values are defined in CustomTextOpType
name (Optional[str]) – Name of the operation used for provenance info (default: function name)
args (Optional[Dict]) – Dictionary containing the arguments of the function if any.

Return type

_CustomTextOperation

Returns

operation – An instance of a custom text operation

class Span(start, end)[source]#

Slice of text extracted from the original text

Parameters

start (int) – Index of the first character in the original text
end (int) – Index of the last character in the original text, plus one

Methods:

`from_dict`(span_dict)	Creates a Span from a dict
`get_subclass_for_data_dict`(data_dict)	Return the subclass that corresponds to the class name found in a data dict
`overlaps`(other)	Test if 2 spans reference at least one character in common

overlaps(other)[source]#

Test if 2 spans reference at least one character in common

classmethod from_dict(span_dict)[source]#

Creates a Span from a dict

Parameters: span_dict (dict) – A dictionary from a serialized span as generated by to_dict()
Return type: Self

classmethod get_subclass_for_data_dict(data_dict)#

Return the subclass that corresponds to the class name found in a data dict

Parameters: data_dict (Dict[str, Any]) – Data dict returned by the to_dict() method of a subclass (or of the base class itself)
Return type: Optional[Type[Self]]
Returns: subclass – Subclass that generated data_dict, or None if data_dict correspond to the base class itself.

class ModifiedSpan(length, replaced_spans)[source]#

Slice of text not present in the original text

Parameters

length (int) – Number of characters
replaced_spans (List[medkit.core.text.span.Span]) – Slices of the original text that this span is replacing

Methods:

`from_dict`(modified_span_dict)	Creates a Modified from a dict
`get_subclass_for_data_dict`(data_dict)	Return the subclass that corresponds to the class name found in a data dict

classmethod from_dict(modified_span_dict)[source]#

Creates a Modified from a dict

Parameters: modified_span_dict (dict) – A dictionary from a serialized ModifiedSpan as generated by to_dict()
Return type: Self

classmethod get_subclass_for_data_dict(data_dict)#

Return the subclass that corresponds to the class name found in a data dict

Parameters: data_dict (Dict[str, Any]) – Data dict returned by the to_dict() method of a subclass (or of the base class itself)
Return type: Optional[Type[Self]]
Returns: subclass – Subclass that generated data_dict, or None if data_dict correspond to the base class itself.

class AnySpan[source]#

Methods:

get_subclass_for_data_dict(data_dict)

Return the subclass that corresponds to the class name found in a data dict

classmethod get_subclass_for_data_dict(data_dict)#

Return the subclass that corresponds to the class name found in a data dict

Parameters: data_dict (Dict[str, Any]) – Data dict returned by the to_dict() method of a subclass (or of the base class itself)
Return type: Optional[Type[Self]]
Returns: subclass – Subclass that generated data_dict, or None if data_dict correspond to the base class itself.

class UMLSNormAttribute(cui, umls_version, term=None, score=None, sem_types=None, metadata=None, uid=None)[source]#

Normalization attribute linking an entity to a CUI in the UMLS knowledge base

Variables

uid – Identifier of the attribute
label – The attribute label, always set to EntityNormAttribute.LABEL
value – CUI prefixed with “umls:” (ex: “umls:C0011849”)
kb_name – Name of the knowledge base. Always “umls”
kb_id – CUI (Concept Unique Identifier) to which the annotation should be linked
cui – Convenience alias of kb_id
kb_version – Version of the UMLS database (ex: “202AB”)
umls_version – Convenience alias of kb_version
term – Optional normalized version of the entity text
score – Optional score reflecting confidence of this link
sem_types (Optional[List[str]]) – Optional IDs of semantic types of the CUI (ex: [“T047”])
metadata – Metadata of the attribute

Methods:

`copy`()	Create a new attribute that is a copy of the current instance, but with a new identifier
`from_dict`(data)	Creates an Attribute from a dict
`get_subclass_for_data_dict`(data_dict)	Return the subclass that corresponds to the class name found in a data dict
`to_brat`()	Return a value compatible with the brat format
`to_spacy`()	Return a value compatible with spaCy

copy()#

Create a new attribute that is a copy of the current instance, but with a new identifier

This is used when we want to duplicate an existing attribute onto a different annotation.

Return type: Attribute

classmethod from_dict(data)[source]#

Creates an Attribute from a dict

Parameters: attribute_dict (dict) – A dictionary from a serialized Attribute as generated by to_dict()
Return type: Self

classmethod get_subclass_for_data_dict(data_dict)#

Return the subclass that corresponds to the class name found in a data dict

Parameters: data_dict (Dict[str, Any]) – Data dict returned by the to_dict() method of a subclass (or of the base class itself)
Return type: Optional[Type[Self]]
Returns: subclass – Subclass that generated data_dict, or None if data_dict correspond to the base class itself.

to_brat()#

Return a value compatible with the brat format

Return type: str

to_spacy()#

Return a value compatible with spaCy

Return type: str

Subpackages / Submodules#

`medkit.core.text.annotation`
`medkit.core.text.annotation_container`
`medkit.core.text.document`
`medkit.core.text.entity_attribute_container`
`medkit.core.text.entity_norm_attribute`
`medkit.core.text.operation`
`medkit.core.text.span`
`medkit.core.text.span_utils`
`medkit.core.text.umls_norm_attribute`
`medkit.core.text.utils`

medkit.core.text

Contents

medkit.core.text#

APIs#

Subpackages / Submodules#