medkit.text.context.negation_detector

Classes

NegationDetectorRule	Regexp-based rule to use with NegationDetector.
NegationMetadata	Metadata dict added to negation attributes with True value.
NegationDetector	Annotator creating negation attributes.

Module Contents

class medkit.text.context.negation_detector.NegationDetectorRule

Regexp-based rule to use with NegationDetector.

Input text may be converted before detecting rule.

Parameters:

regexpstr

The regexp pattern used to match a negation

exclusion_regexpslist of str, optional

Optional exclusion patterns

idstr, optional

Unique identifier of the rule to store in the metadata of the entities

case_sensitivebool, default=False

Whether to consider case when running regexp and `exclusion_regexs

unicode_sensitivebool, default=False

If True, rule matches are searched on unicode text. So, regexp and `exclusion_regexs shall not contain non-ASCII chars because they would never be matched. If False, rule matches are searched on closest ASCII text when possible. (cf. NegationDetector)

regexp: str

exclusion_regexps: list[str]

id: str | None = None

case_sensitive: bool = False

unicode_sensitive: bool = False

__post_init__()

class medkit.text.context.negation_detector.NegationMetadata

Bases: typing_extensions.TypedDict

Metadata dict added to negation attributes with True value.

Parameters:

rule_idstr or int

Identifier of the rule used to detect a negation. If the rule has no uid, then the index of the rule in the list of rules is used instead.

rule_id: str | int

class medkit.text.context.negation_detector.NegationDetector(output_label: str, rules: list[NegationDetectorRule] | None = None, uid: str | None = None)

Bases: medkit.core.text.ContextOperation

Annotator creating negation attributes.

Because negation attributes will be attached to whole annotations, each input annotation should be “local”-enough rather than a big chunk of text (ie a sentence or a syntagma).

For detecting negation, the module uses rules that may be sensitive to unicode or not. When the rule is not sensitive to unicode, we try to convert unicode chars to the closest ascii chars. However, some characters need to be pre-processed before (e.g., n° -> number). So, if the text lengths are different, we fall back on initial unicode text for detection even if rule is not unicode-sensitive. In this case, a warning is logged for recommending to pre-process data.

init_args

output_label

rules

_non_empty_text_pattern

_patterns

_exclusion_patterns

_has_non_unicode_sensitive_rule

run(segments: list[medkit.core.text.Segment])

Run the operation.

Add a negation attribute to each segment with a boolean value indicating if a hypothesis has been found.

Negation attributes with a True value have a metadata dict with fields described in NegationRuleMetadata.

Parameters:

segmentslist of Segment

List of segments to detect as being negated or not

_detect_negation_in_segment(segment: medkit.core.text.Segment) → medkit.core.Attribute | None

_find_matching_rule(text: str) → str | int | None

static load_rules(path_to_rules: pathlib.Path, encoding: str | None = None) → list[NegationDetectorRule]

Load all rules stored in a yml file.

Parameters:

path_to_rulesPath

Path to a yml file containing a list of mappings with the same structure as NegationDetectorRule

encodingstr, optional

Encoding of the file to open

Returns:

list of NegationDetectorRule

List of all the rules in path_to_rules, can be used to init a NegationDetector

static check_rules_sanity(rules: list[NegationDetectorRule])

Check consistency of a set of rules.

static save_rules(rules: list[NegationDetectorRule], path_to_rules: pathlib.Path, encoding: str | None = None)

Store rules in a yml file.

Parameters:

ruleslist of NegationDetectorRule

The rules to save

path_to_rulesPath

Path to a .yml file that will contain the rules

encodingstr, optional

Encoding of the .yml file