spaCy/bin/wiki_entity_linking/README.md

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
  • WikiData: get latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/
  • Wikipedia: get enwiki-latest-pages-articles-multistream.xml.bz2 from https://dumps.wikimedia.org/enwiki/latest/ (or for any other language)
• You can set the filtering parameters for KB construction:
  • max_per_alias (-a): (max) number of candidate entities in the KB per alias/synonym
  • min_freq (-f): threshold of number of times an entity should occur in the corpus to be included in the KB
  • min_pair (-c): 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 (-wp): whether to parse descriptions from Wikipedia (True) or Wikidata (False)
  • entity_vector_length (-v): length of the pre-trained entity description vectors
  • lang (-la): 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 (-lp), limit_train (-lt) and/or limit_wd (-lw) to read only parts of the dumps instead of everything.
  • e.g. set -lt 20000 -lp 2000 -lw 3000 -f 1
• 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
• Specify the output directory (-o) in which the final, trained model will be saved
• You can set the learning parameters for the EL training:
  • epochs (-e): number of training iterations
  • dropout (-p): dropout rate
  • lr (-n): learning rate
  • l2 (-r): L2 regularization
• Specify the number of training and dev testing articles with train_articles (-t) and dev_articles (-d) respectively
  • If not specified, the full dataset will be processed - this may take a LONG time !
• Further parameters to set:
  • labels_discard (-l): NER label types to discard during training