mirror of https://github.com/explosion/spaCy.git
2d249a9502
* 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 |
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| .. | ||
| README.md | ||
| __init__.py | ||
| entity_linker_evaluation.py | ||
| kb_creator.py | ||
| train_descriptions.py | ||
| wiki_io.py | ||
| wiki_namespaces.py | ||
| wikidata_pretrain_kb.py | ||
| wikidata_processor.py | ||
| wikidata_train_entity_linker.py | ||
| wikipedia_processor.py | ||
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.bz2from https://dumps.wikimedia.org/wikidatawiki/entities/ - Wikipedia: get
enwiki-latest-pages-articles-multistream.xml.bz2from https://dumps.wikimedia.org/enwiki/latest/ (or for any other language)
- WikiData: get
- You can set the filtering parameters for KB construction:
max_per_alias: (max) number of candidate entities in the KB per alias/synonymmin_freq: threshold of number of times an entity should occur in the corpus to be included in the KBmin_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 vectorslang: 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_trainand/orlimit_wdto 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 iterationsdropout: dropout ratelr: learning ratel2: L2 regularization
- Specify the number of training and dev testing entities with
train_instanddev_instrespectively - Further parameters to set:
labels_discard: NER label types to discard during training