medkit.text.context.hypothesis_detector#

Classes:

HypothesisDetector([output_label, rules, ...])

Annotator creating hypothesis Attributes with boolean values indicating if an hypothesis has been found.

HypothesisDetectorRule(regexp[, ...])

Regexp-based rule to use with HypothesisDetector

HypothesisRuleMetadata(_typename[, _fields])

Metadata dict added to hypothesis attributes with True value detected by a rule

HypothesisVerbMetadata(_typename[, _fields])

Metadata dict added to hypothesis attributes with True value detected by a rule.
class HypothesisDetector(output_label='hypothesis', rules=None, verbs=None, modes_and_tenses=None, max_length=150, uid=None)[source]#

Annotator creating hypothesis Attributes with boolean values indicating if an hypothesis has been found.

Hypothesis will be considered present either because of the presence of a certain text pattern in a segment, or because of the usage of a certain verb at a specific mode and tense (for instance conditional).

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

Instantiate the hypothesis detector

Parameters

  • output_label (str) – The label of the created attributes

  • rules (Optional[List[HypothesisDetectorRule]]) – The set of rules to use when detecting hypothesis. If none provided, the rules in “hypothesis_detector_default_rules.yml” will be used

  • verbs (Optional[Dict[str, Dict[str, Dict[str, List[str]]]]]) – Conjugated verbs forms, to be used in association with modes_and_tenses. Conjugated forms of a verb at a specific mode and tense must be provided in nested dicts with the 1st key being the verb’s root, the 2d key the mode and the 3d key the tense. For instance verb[“aller”][“indicatif][“présent”] would hold the list [“vais”, “vas”, “va”, “allons”, aller”, “vont”] When verbs is provided, modes_and_tenses must also be provided. If none provided, the rules in “hypothesis_detector_default_verbs.yml” will be used.

  • modes_and_tenses (Optional[List[Tuple[str, str]]]) – List of tuples of all modes and tenses associated with hypothesis. Will be used to select conjugated forms in verbs that denote hypothesis.

  • max_length (int) – Maximum number of characters in a hypothesis segment. Segments longer than this will never be considered as hypothesis

  • uid (str) – Identifier of the detector

Methods:

check_rules_sanity(rules)

Check consistency of a set of rules

get_example()

Instantiate an HypothesisDetector with example rules and verbs, designed for usage with EDS documents

load_rules(path_to_rules[, encoding])

Load all rules stored in a yml file

load_verbs(path_to_verbs[, encoding])

Load all conjugated verb forms stored in a yml file.

run(segments)

Add an hypothesis attribute to each segment with a boolean value indicating if an hypothesis has been detected.

save_rules(rules, path_to_rules[, encoding])

Store rules in a yml file

set_prov_tracer(prov_tracer)

Enable provenance tracing.

Attributes:

description

Contains all the operation init parameters.
run(segments)[source]#

Add an hypothesis attribute to each segment with a boolean value indicating if an hypothesis has been detected.

Hypothesis attributes with a True value have a metadata dict with fields described in either HypothesisRuleMetadata or HypothesisVerbMetadata.

Parameters

segments (List[Segment]) – List of segments to detect as being hypothesis or not

static load_verbs(path_to_verbs, encoding=None)[source]#

Load all conjugated verb forms stored in a yml file. Conjugated verb forms at a specific mode and tense must be stored in nested mappings with the 1st key being the verb root, the 2d key the mode and the 3d key the tense.

Parameters

  • path_to_verbs (Path) – Path to a yml file containing a list of verbs form, arranged by mode and tense.

  • encoding (Optional[str]) – Encoding on the file to open

Return type

Dict[str, Dict[str, Dict[str, List[str]]]]

Returns

List[Dict[str, Dict[str, List[str]]]] – List of verb forms in path_to_verbs, can be used to init an HypothesisDetector

static load_rules(path_to_rules, encoding=None)[source]#

Load all rules stored in a yml file

Parameters

  • path_to_rules (Path) – Path to a yml file containing a list of mappings with the same structure as HypothesisDetectorRule

  • encoding (Optional[str]) – Encoding of the file to open

Return type

List[HypothesisDetectorRule]

Returns

List[HypothesisDetectorRule] – List of all the rules in path_to_rules, can be used to init an HypothesisDetector

classmethod get_example()[source]#

Instantiate an HypothesisDetector with example rules and verbs, designed for usage with EDS documents

Return type

HypothesisDetector

static check_rules_sanity(rules)[source]#

Check consistency of a set of rules

static save_rules(rules, path_to_rules, encoding=None)[source]#

Store rules in a yml file

Parameters

  • rules (List[HypothesisDetectorRule]) – The rules to save

  • path_to_rules (Path) – Path to a .yml file that will contain the rules

  • encoding (Optional[str]) – Encoding of the .yml file

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 HypothesisDetectorRule(regexp, exclusion_regexps=<factory>, id=None, case_sensitive=False, unicode_sensitive=False)[source]#

Regexp-based rule to use with HypothesisDetector

Variables

  • regexp (str) – The regexp pattern used to match a hypothesis

  • exclusion_regexps (List[str]) – Optional exclusion patterns

  • id (Optional[str]) – Unique identifier of the rule to store in the metadata of the entities

  • case_sensitive (bool) – Whether to ignore case when running regexp and `exclusion_regexps

  • unicode_sensitive (bool) – Whether to replace all non-ASCII chars by the closest ASCII chars on input text before running regexp and `exclusion_regexps. If True, then regexp and `exclusion_regexps shouldn’t contain non-ASCII chars because they would never be matched.

class HypothesisRuleMetadata(_typename, _fields=None, /, **kwargs)[source]#

Metadata dict added to hypothesis attributes with True value detected by a rule

Parameters

  • type (Literal['rule']) – Metadata type, here “rule” (use to differentiate between rule/verb metadata dict)

  • rule_id (str) – Identifier of the rule used to detect an hypothesis. If the rule has no uid, then the index of the rule in the list of rules is used instead

class HypothesisVerbMetadata(_typename, _fields=None, /, **kwargs)[source]#

Metadata dict added to hypothesis attributes with True value detected by a rule.

Parameters

  • type (Literal['verb']) – Metadata type, here “verb” (use to differentiate between rule/verb metadata dict).

  • matched_verb (str) – Root of the verb used to detect an hypothesis.