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* 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
35 lines
2.0 KiB
Markdown
35 lines
2.0 KiB
Markdown
## Entity Linking with Wikipedia and Wikidata
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### Step 1: Create a Knowledge Base (KB) and training data
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Run `wikipedia_pretrain_kb.py`
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* This takes as input the locations of a **Wikipedia and a Wikidata dump**, and produces a **KB directory** + **training file**
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* WikiData: get `latest-all.json.bz2` from https://dumps.wikimedia.org/wikidatawiki/entities/
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* Wikipedia: get `enwiki-latest-pages-articles-multistream.xml.bz2` from https://dumps.wikimedia.org/enwiki/latest/ (or for any other language)
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* You can set the filtering parameters for KB construction:
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* `max_per_alias`: (max) number of candidate entities in the KB per alias/synonym
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* `min_freq`: threshold of number of times an entity should occur in the corpus to be included in the KB
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* `min_pair`: threshold of number of times an entity+alias combination should occur in the corpus to be included in the KB
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* Further parameters to set:
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* `descriptions_from_wikipedia`: whether to parse descriptions from Wikipedia (`True`) or Wikidata (`False`)
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* `entity_vector_length`: length of the pre-trained entity description vectors
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* `lang`: language for which to fetch Wikidata information (as the dump contains all languages)
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Quick testing and rerunning:
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* 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.
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* If you only want to (re)run certain parts of the pipeline, just remove the corresponding files and they will be recalculated or reparsed.
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### Step 2: Train an Entity Linking model
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Run `wikidata_train_entity_linker.py`
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* This takes the **KB directory** produced by Step 1, and trains an **Entity Linking model**
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* You can set the learning parameters for the EL training:
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* `epochs`: number of training iterations
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* `dropout`: dropout rate
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* `lr`: learning rate
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* `l2`: L2 regularization
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* Specify the number of training and dev testing entities with `train_inst` and `dev_inst` respectively
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* Further parameters to set:
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* `labels_discard`: NER label types to discard during training
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