# nlp_architect.data.cdc_resources.relations package

## Submodules

## nlp_architect.data.cdc_resources.relations.computed_relation_extraction module

class `nlp_architect.data.cdc_resources.relations.computed_relation_extraction.``ComputedRelationExtraction`

  Bases: `nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction`

  Extract Relation between two mentions according to computation and rule based algorithms

  `extract_all_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]

    Try to find if mentions has anyone or more of the relations this class support

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight
    Returns: | One or more of: RelationType.EXACT_STRING, RelationType.FUZZY_FIT, RelationType.FUZZY_HEAD_FIT, RelationType.SAME_HEAD_LEMMA, RelationType.SAME_HEAD_LEMMA_RELAX
    Return type: | Set[RelationType]

  static `extract_exact_string`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has exact string relation

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight
    Returns: | RelationType.EXACT_STRING or RelationType.NO_RELATION_FOUND

  static `extract_fuzzy_fit`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has fuzzy fit relation

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight
    Returns: | RelationType.FUZZY_FIT or RelationType.NO_RELATION_FOUND

  static `extract_fuzzy_head_fit`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has fuzzy head fit relation

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight
    Returns: | RelationType.FUZZY_HEAD_FIT or RelationType.NO_RELATION_FOUND

  static `extract_same_head_lemma`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has same head lemma relation

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight
    Returns: | RelationType.SAME_HEAD_LEMMA or RelationType.NO_RELATION_FOUND

  `extract_sub_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has the given relation between them

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight relation – RelationType
    Returns: | relation in case mentions has given relation or RelationType.NO_RELATION_FOUND otherwise
    Return type: | RelationType

  static `get_supported_relations`() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]

    Return all supported relations by this class

    Returns: | List[RelationType]

## nlp_architect.data.cdc_resources.relations.referent_dict_relation_extraction module

class `nlp_architect.data.cdc_resources.relations.referent_dict_relation_extraction.``ReferentDictRelationExtraction`(method: nlp_architect.data.cdc_resources.relations.relation_types_enums.OnlineOROfflineMethod = <OnlineOROfflineMethod.ONLINE: 'online'>, ref_dict: str = None)

  Bases: `nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction`

  `extract_all_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]
  `extract_sub_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has the given relation between them

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight relation – RelationType
    Returns: | relation in case mentions has given relation or RelationType.NO_RELATION_FOUND otherwise
    Return type: | RelationType

  static `get_supported_relations`()

    Return all supported relations by this class

    Returns: | List[RelationType]

  `is_referent_dict`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → bool

    Check if input mentions has referent dictionary relation between them

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight
    Returns: | bool

  static `load_reference_dict`(dict_fname: str) → Dict[str, List[str]]

    Method to load referent dictionary to memory

    Returns: | List[RelationType]

## nlp_architect.data.cdc_resources.relations.relation_extraction module

class `nlp_architect.data.cdc_resources.relations.relation_extraction.``RelationExtraction`

  Bases: `object`

  `extract_relation`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Base Class Check if Sub class support given relation before executing the sub class

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight relation – RelationType
    Returns: | relation in case mentions has given relation and RelationType.NO_RELATION_FOUND otherwise
    Return type: | RelationType

  `extract_sub_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType
  static `get_supported_relations`() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]

## nlp_architect.data.cdc_resources.relations.relation_types_enums module

class `nlp_architect.data.cdc_resources.relations.relation_types_enums.``EmbeddingMethod`

  Bases: `enum.Enum`

  An enumeration.

  `ELMO` = 'elmo'
  `ELMO_OFFLINE` = 'elmo_offline'
  `GLOVE` = 'glove'
  `GLOVE_OFFLINE` = 'glove_offline'

class `nlp_architect.data.cdc_resources.relations.relation_types_enums.``OnlineOROfflineMethod`

  Bases: `enum.Enum`

  An enumeration.

  `OFFLINE` = 'offline'
  `ONLINE` = 'online'

class `nlp_architect.data.cdc_resources.relations.relation_types_enums.``RelationType`

  Bases: `enum.Enum`

  An enumeration.

  `EXACT_STRING` = 8
  `FUZZY_FIT` = 9
  `FUZZY_HEAD_FIT` = 10
  `NO_RELATION_FOUND` = 0
  `OTHER` = 21
  `REFERENT_DICT` = 18
  `SAME_HEAD_LEMMA` = 11
  `VERBOCEAN_MATCH` = 13
  `WIKIPEDIA_ALIASES` = 2
  `WIKIPEDIA_BE_COMP` = 7
  `WIKIPEDIA_CATEGORY` = 5
  `WIKIPEDIA_DISAMBIGUATION` = 3
  `WIKIPEDIA_PART_OF_SAME_NAME` = 4
  `WIKIPEDIA_REDIRECT_LINK` = 1
  `WIKIPEDIA_TITLE_PARENTHESIS` = 6
  `WITHIN_DOC_COREF` = 20
  `WORDNET_DERIVATIONALLY` = 14
  `WORDNET_PARTIAL_SYNSET_MATCH` = 15
  `WORDNET_SAME_SYNSET` = 17
  `WORD_EMBEDDING_MATCH` = 19

class `nlp_architect.data.cdc_resources.relations.relation_types_enums.``WikipediaSearchMethod`

  Bases: `enum.Enum`

  An enumeration.

  `ELASTIC` = 'elastic'
  `OFFLINE` = 'offline'
  `ONLINE` = 'online'

## nlp_architect.data.cdc_resources.relations.verbocean_relation_extraction module

class `nlp_architect.data.cdc_resources.relations.verbocean_relation_extraction.``VerboceanRelationExtraction`(method: nlp_architect.data.cdc_resources.relations.relation_types_enums.OnlineOROfflineMethod = <OnlineOROfflineMethod.ONLINE: 'online'>, vo_file: str = None)

  Bases: `nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction`

  `extract_all_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]
  `extract_sub_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has the given relation between them

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight relation – RelationType
    Returns: | relation in case mentions has given relation or RelationType.NO_RELATION_FOUND otherwise
    Return type: | RelationType

  static `get_supported_relations`()

    Return all supported relations by this class

    Returns: | List[RelationType]

  `is_verbocean_relation`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → bool

    Check if input mentions has VerbOcean relation between them

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight
    Returns: | bool

  static `load_verbocean_file`(fname: str) → Dict[str, Dict[str, str]]

    Method to load referent dictionary to memory

    Returns: | List[RelationType]

## nlp_architect.data.cdc_resources.relations.wikipedia_relation_extraction module

class `nlp_architect.data.cdc_resources.relations.wikipedia_relation_extraction.``WikipediaRelationExtraction`(method: nlp_architect.data.cdc_resources.relations.relation_types_enums.WikipediaSearchMethod = <WikipediaSearchMethod.ONLINE: 'online'>, wiki_file: str = None, host: str = None, port: int = None, index: str = None, filter_pronouns: bool = True, filter_time_data: bool = True)

  Bases: `nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction`

  static `extract_aliases`(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, titles1: Set[str], titles2: Set[str]) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has aliases relation

    Parameters: | pages1 – WikipediaPages pages2 – WikipediaPage titles1 – Set[str] titles2 – Set[str]
    Returns: | RelationType.WIKIPEDIA_ALIASES or RelationType.NO_RELATION_FOUND

  `extract_all_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]

    Try to find if mentions has anyone or more of the relations this class support

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight
    Returns: | One or more of: RelationType.WIKIPEDIA_BE_COMP, RelationType.WIKIPEDIA_TITLE_PARENTHESIS, RelationType.WIKIPEDIA_DISAMBIGUATION, RelationType.WIKIPEDIA_CATEGORY, RelationType.WIKIPEDIA_REDIRECT_LINK, RelationType.WIKIPEDIA_ALIASES, RelationType.WIKIPEDIA_PART_OF_SAME_NAME
    Return type: | Set[RelationType]

  static `extract_be_comp`(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, titles1: Set[str], titles2: Set[str]) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has be-comp/is-a relation

    Parameters: | pages1 – WikipediaPages pages2 – WikipediaPage titles1 – Set[str] titles2 – Set[str]
    Returns: | RelationType.WIKIPEDIA_BE_COMP or RelationType.NO_RELATION_FOUND

  static `extract_category`(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, titles1: Set[str], titles2: Set[str]) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has category relation

    Parameters: | pages1 – WikipediaPages pages2 – WikipediaPage titles1 – Set[str] titles2 – Set[str]
    Returns: | RelationType.WIKIPEDIA_CATEGORY or RelationType.NO_RELATION_FOUND

  static `extract_disambig`(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, titles1: Set[str], titles2: Set[str]) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has disambiguation relation

    Parameters: | pages1 – WikipediaPages pages2 – WikipediaPage titles1 – Set[str] titles2 – Set[str]
    Returns: | RelationType.WIKIPEDIA_DISAMBIGUATION or RelationType.NO_RELATION_FOUND

  static `extract_parenthesis`(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, titles1: Set[str], titles2: Set[str]) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has parenthesis relation

    Parameters: | pages1 – WikipediaPages pages2 – WikipediaPage titles1 – Set[str] titles2 – Set[str]
    Returns: | RelationType.WIKIPEDIA_TITLE_PARENTHESIS or RelationType.NO_RELATION_FOUND

  `extract_sub_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has the given relation between them

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight relation – RelationType
    Returns: | relation in case mentions has given relation or RelationType.NO_RELATION_FOUND otherwise
    Return type: | RelationType

  `get_phrase_related_pages`(mention_str: str) → nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages

    Get all WikipediaPages pages related with this mention string

    Parameters: | mention_str – str
    Returns: | WikipediaPages

  static `get_supported_relations`() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]

    Return all supported relations by this class

    Returns: | List[RelationType]

  static `is_both_data_or_time`(mention1: nlp_architect.common.cdc.mention_data.MentionDataLight, mention2: nlp_architect.common.cdc.mention_data.MentionDataLight) → bool

    check if both phrases refers to time or date

    Returns: | bool

  static `is_both_opposite_personal_pronouns`(phrase1: str, phrase2: str) → bool

    check if both phrases refers to pronouns

    Returns: | bool

  `is_part_of_same_name`(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages) → bool

    Check if input mentions has part of same name relation (eg: page1=John, page2=Smith)

    Parameters: | pages1 – WikipediaPages pages2 – WikipediaPage
    Returns: | bool

  static `is_redirect_same`(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages) → bool

    Check if input mentions has same wikipedia redirect page

    Parameters: | pages1 – WikipediaPages pages2 – WikipediaPage
    Returns: | bool

## nlp_architect.data.cdc_resources.relations.within_doc_coref_extraction module

class `nlp_architect.data.cdc_resources.relations.within_doc_coref_extraction.``WithinDocCoref`(wd_file: str)

  Bases: `nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction`

  static `arrange_resource`(wd_mentions_json)
  static `create_ment_id`(mention_x: nlp_architect.common.cdc.mention_data.MentionData, mention_y: nlp_architect.common.cdc.mention_data.MentionData) → str
  `extract_all_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionData, mention_y: nlp_architect.common.cdc.mention_data.MentionData) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]
  `extract_sub_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionData, mention_y: nlp_architect.common.cdc.mention_data.MentionData, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has the given relation between them

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight relation – RelationType
    Returns: | relation in case mentions has given relation or RelationType.NO_RELATION_FOUND otherwise
    Return type: | RelationType

  `extract_within_coref`(mention: nlp_architect.common.cdc.mention_data.MentionData) → List[str]
  static `get_supported_relations`() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]

    Return all supported relations by this class

    Returns: | List[RelationType]

  `get_within_doc_coref_chain`()

## nlp_architect.data.cdc_resources.relations.word_embedding_relation_extraction module

class `nlp_architect.data.cdc_resources.relations.word_embedding_relation_extraction.``WordEmbeddingRelationExtraction`(method: nlp_architect.data.cdc_resources.relations.relation_types_enums.EmbeddingMethod = <EmbeddingMethod.GLOVE: 'glove'>, glove_file: str = None, elmo_file: str = None, cos_accepted_dist: float = 0.7)

  Bases: `nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction`

  `extract_all_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]
  `extract_sub_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has the given relation between them

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight relation – RelationType
    Returns: | relation in case mentions has given relation or RelationType.NO_RELATION_FOUND otherwise
    Return type: | RelationType

  static `get_supported_relations`() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]

    Return all supported relations by this class

    Returns: | List[RelationType]

  `is_word_embed_match`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight)

    Check if input mentions Word Embedding cosine distance below above 0.65

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight
    Returns: | bool

## nlp_architect.data.cdc_resources.relations.wordnet_relation_extraction module

class `nlp_architect.data.cdc_resources.relations.wordnet_relation_extraction.``WordnetRelationExtraction`(method: nlp_architect.data.cdc_resources.relations.relation_types_enums.OnlineOROfflineMethod = <OnlineOROfflineMethod.ONLINE: 'online'>, wn_file: str = None)

  Bases: `nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction`

  `extract_all_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]

    Try to find if mentions has anyone or more of the relations this class support

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight
    Returns: | One or more of: RelationType.WORDNET_SAME_SYNSET_ENTITY, RelationType.WORDNET_SAME_SYNSET_EVENT, RelationType.WORDNET_PARTIAL_SYNSET_MATCH, RelationType.WORDNET_DERIVATIONALLY
    Return type: | Set[RelationType]

  static `extract_derivation`(page_x: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage, page_y: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has derivation relation

    Parameters: | page_x – WordnetPage page_y – WordnetPage
    Returns: | RelationType.WORDNET_DERIVATIONALLY or RelationType.NO_RELATION_FOUND

  static `extract_partial_synset_match`(page_x: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage, page_y: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has partial synset relation

    Parameters: | page_x – WordnetPage page_y – WordnetPage
    Returns: | RelationType.WORDNET_PARTIAL_SYNSET_MATCH or RelationType.NO_RELATION_FOUND

  static `extract_same_synset_entity`(page_x: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage, page_y: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has same synset relation for entity mentions

    Parameters: | page_x – WordnetPage page_y – WordnetPage
    Returns: | RelationType.WORDNET_SAME_SYNSET_ENTITY or RelationType.NO_RELATION_FOUND

  `extract_sub_relations`(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType

    Check if input mentions has the given relation between them

    Parameters: | mention_x – MentionDataLight mention_y – MentionDataLight relation – RelationType
    Returns: | relation in case mentions has given relation or RelationType.NO_RELATION_FOUND otherwise
    Return type: | RelationType

  static `get_supported_relations`() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType]

    Return all supported relations by this class

    Returns: | List[RelationType]

## Module contents