# nlp_architect.models.cross_doc_coref package

## Subpackages

- nlp_architect.models.cross_doc_coref.system package
  - Subpackages
    - nlp_architect.models.cross_doc_coref.system.sieves package
      - Submodules
      - nlp_architect.models.cross_doc_coref.system.sieves.run_sieve_system module
      - nlp_architect.models.cross_doc_coref.system.sieves.sieves module
      - Module contents
  - Submodules
  - nlp_architect.models.cross_doc_coref.system.cdc_utils module
  - nlp_architect.models.cross_doc_coref.system.sieves_container_init module
  - Module contents

## Submodules

## nlp_architect.models.cross_doc_coref.sieves_config module

class `nlp_architect.models.cross_doc_coref.sieves_config.``EntitySievesConfiguration`

  Bases: `nlp_architect.models.cross_doc_coref.sieves_config.SievesConfiguration`

class `nlp_architect.models.cross_doc_coref.sieves_config.``EventSievesConfiguration`

  Bases: `nlp_architect.models.cross_doc_coref.sieves_config.SievesConfiguration`

class `nlp_architect.models.cross_doc_coref.sieves_config.``SievesConfiguration`

  Bases: `object`

  `run_evaluation`

    Should run evaluation (True/False)

  `sieves_order`

    Sieve definition and Sieve running order

    Tuple[SieveType, RelationType, Threshold(float)] - define sieves to run, were

    Strict- Merge clusters only in case all mentions has current relation between them, Relax- Merge clusters in case (matched mentions) / len(cluster_1.mentions)) >= thresh, Very_Relax- Merge clusters in case (matched mentions) / (all possible pairs) >= thresh

    RelationType represent the type of sieve to run.

## nlp_architect.models.cross_doc_coref.sieves_resource module

class `nlp_architect.models.cross_doc_coref.sieves_resource.``SievesResources`

  Bases: `object`

  `elastic_host`

    Elastic host, #Required when using Elastic evaluation using Wikipedia sieve

  `elastic_index`

    Elastic index name, #Required when using Elastic evaluation using Wikipedia sieve

  `elastic_port`

    Elastic port number, #Required when using Elastic evaluation using Wikipedia sieve

  `elmo_file`

    Location of Elmo mini data set file, #Required mini data set file location for Offline evaluation using GloVe sieve

  `embed_search_method`

    EmbeddingMethod.GLOVE, EmbeddingMethod.GLOVE_OFFLINE, EmbeddingMethod.ELMO, EmbeddingMethod.ELMO_OFFLINE

    Type: | Wordnet search method type, one of

  `eval_output_dir`

    The output dir of the evaluation files, here scorer file for cross doc coref spans will be saved

  `glove_file`

    Location of GloVe mini data set file, #Required mini data set file location for Offline evaluation using GloVe sieve

  `referent_dict_file`

    Location of Referent dic data set file, #Required mini data set file for Offline evaluation or original file for Online evaluation using Referent Dict sieve

  `referent_dict_method`

    OnlineOROfflineMethod.ONLINE, OnlineOROfflineMethod.OFFLINE

    Type: | Referent Dict search method type, one of

  `vo_dict_file`

    Location of VerbOcean data set file, #Required mini data set file for Offline evaluation or original file for Online evaluation using VerbOcean sieve

  `vo_search_method`

    OnlineOROfflineMethod.ONLINE, OnlineOROfflineMethod.OFFLINE

    Type: | VerbOcean search method type, one of

  `wd_file`

    Location of Within doc data set file, #Required when using Within doc sieve

  `wiki_folder`

    Location of Wikipedia mini data set file, #Required mini data set file location for Offline evaluation using Wikipedia sieve

  `wiki_search_method`

    WikipediaSearchMethod.ONLINE, WikipediaSearchMethod.OFFLINE, WikipediaSearchMethod.ELASTIC

    Type: | Wikipedia search method type, one of

  `wn_folder`

    Location of WordNet mini data set file, #Required mini data set file location for Offline evaluation using WordNet sieve

  `wn_search_method`

    OnlineOROfflineMethod.ONLINE, OnlineOROfflineMethod.OFFLINE

    Type: | Wordnet search method type, one of

## Module contents