explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-12-26 18:06:29 +03:00
Code Issues Packages Projects Releases Wiki Activity
054df5d90a
spaCy/bin/wiki_entity_linking/README.md
Sofie Van Landeghem 2d249a9502 KB extensions and better parsing of WikiData (#4375) 
* fix overflow error on windows

* more documentation & logging fixes

* md fix

* 3 different limit parameters to play with execution time

* bug fixes directory locations

* small fixes

* exclude dev test articles from prior probabilities stats

* small fixes

* filtering wikidata entities, removing numeric and meta items

* adding aliases from wikidata also to the KB

* fix adding WD aliases

* adding also new aliases to previously added entities

* fixing comma's

* small doc fixes

* adding subclassof filtering

* append alias functionality in KB

* prevent appending the same entity-alias pair

* fix for appending WD aliases

* remove date filter

* remove unnecessary import

* small corrections and reformatting

* remove WD aliases for now (too slow)

* removing numeric entities from training and evaluation

* small fixes

* shortcut during prediction if there is only one candidate

* add counts and fscore logging, remove FP NER from evaluation

* fix entity_linker.predict to take docs instead of single sentences

* remove enumeration sentences from the WP dataset

* entity_linker.update to process full doc instead of single sentence

* spelling corrections and dump locations in readme

* NLP IO fix

* reading KB is unnecessary at the end of the pipeline

* small logging fix

* remove empty files
2019-10-14 12:28:53 +02:00

2.0 KiB
Raw Blame History

Entity Linking with Wikipedia and Wikidata

Step 1: Create a Knowledge Base (KB) and training data

Run wikipedia_pretrain_kb.py

  • This takes as input the locations of a Wikipedia and a Wikidata dump, and produces a KB directory + training file
  • You can set the filtering parameters for KB construction:
    • max_per_alias: (max) number of candidate entities in the KB per alias/synonym
    • min_freq: threshold of number of times an entity should occur in the corpus to be included in the KB
    • min_pair: threshold of number of times an entity+alias combination should occur in the corpus to be included in the KB
  • Further parameters to set:
    • descriptions_from_wikipedia: whether to parse descriptions from Wikipedia (True) or Wikidata (False)
    • entity_vector_length: length of the pre-trained entity description vectors
    • lang: language for which to fetch Wikidata information (as the dump contains all languages)

Quick testing and rerunning:

  • When trying out the pipeline for a quick test, set limit_prior, limit_train and/or limit_wd to read only parts of the dumps instead of everything.
  • If you only want to (re)run certain parts of the pipeline, just remove the corresponding files and they will be recalculated or reparsed.

Step 2: Train an Entity Linking model

Run wikidata_train_entity_linker.py

  • This takes the KB directory produced by Step 1, and trains an Entity Linking model
  • You can set the learning parameters for the EL training:
    • epochs: number of training iterations
    • dropout: dropout rate
    • lr: learning rate
    • l2: L2 regularization
  • Specify the number of training and dev testing entities with train_inst and dev_inst respectively
  • Further parameters to set:
    • labels_discard: NER label types to discard during training