spaCy/examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py

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# coding: utf-8
from __future__ import unicode_literals
from examples.pipeline.wiki_entity_linking import wikipedia_processor as wp, kb_creator, training_set_creator, run_el
from examples.pipeline.wiki_entity_linking.train_el import EL_Model
import spacy
from spacy.vocab import Vocab
from spacy.kb import KnowledgeBase
import datetime
"""
Demonstrate how to build a knowledge base from WikiData and run an Entity Linking algorithm.
"""
PRIOR_PROB = 'C:/Users/Sofie/Documents/data/wikipedia/prior_prob.csv'
ENTITY_COUNTS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_freq.csv'
ENTITY_DEFS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_defs.csv'
ENTITY_DESCR = 'C:/Users/Sofie/Documents/data/wikipedia/entity_descriptions.csv'
KB_FILE = 'C:/Users/Sofie/Documents/data/wikipedia/kb'
VOCAB_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/vocab'
TRAINING_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/training_data_nel/'
if __name__ == "__main__":
print("START", datetime.datetime.now())
print()
my_kb = None
# one-time methods to create KB and write to file
to_create_prior_probs = False
to_create_entity_counts = False
to_create_kb = False
# read KB back in from file
to_read_kb = True
to_test_kb = False
# create training dataset
create_wp_training = False
# run training
run_training = True
# apply named entity linking to the dev dataset
apply_to_dev = False
# STEP 1 : create prior probabilities from WP
# run only once !
if to_create_prior_probs:
print("STEP 1: to_create_prior_probs", datetime.datetime.now())
wp.read_wikipedia_prior_probs(prior_prob_output=PRIOR_PROB)
print()
# STEP 2 : deduce entity frequencies from WP
# run only once !
if to_create_entity_counts:
print("STEP 2: to_create_entity_counts", datetime.datetime.now())
wp.write_entity_counts(prior_prob_input=PRIOR_PROB, count_output=ENTITY_COUNTS, to_print=False)
print()
# STEP 3 : create KB and write to file
# run only once !
if to_create_kb:
print("STEP 3a: to_create_kb", datetime.datetime.now())
my_nlp = spacy.load('en_core_web_sm')
my_vocab = my_nlp.vocab
my_kb = kb_creator.create_kb(my_vocab,
max_entities_per_alias=10,
min_occ=5,
entity_def_output=ENTITY_DEFS,
entity_descr_output=ENTITY_DESCR,
count_input=ENTITY_COUNTS,
prior_prob_input=PRIOR_PROB,
to_print=False)
print("kb entities:", my_kb.get_size_entities())
print("kb aliases:", my_kb.get_size_aliases())
print()
print("STEP 3b: write KB", datetime.datetime.now())
my_kb.dump(KB_FILE)
my_vocab.to_disk(VOCAB_DIR)
print()
# STEP 4 : read KB back in from file
if to_read_kb:
print("STEP 4: to_read_kb", datetime.datetime.now())
my_vocab = Vocab()
my_vocab.from_disk(VOCAB_DIR)
my_kb = KnowledgeBase(vocab=my_vocab)
my_kb.load_bulk(KB_FILE)
print("kb entities:", my_kb.get_size_entities())
print("kb aliases:", my_kb.get_size_aliases())
print()
# test KB
if to_test_kb:
my_nlp = spacy.load('en_core_web_sm')
run_el.run_el_toy_example(kb=my_kb, nlp=my_nlp)
print()
# STEP 5: create a training dataset from WP
if create_wp_training:
print("STEP 5: create training dataset", datetime.datetime.now())
training_set_creator.create_training(kb=my_kb, entity_def_input=ENTITY_DEFS, training_output=TRAINING_DIR)
# STEP 6: apply the EL algorithm on the training dataset
if run_training:
print("STEP 6: training ", datetime.datetime.now())
my_nlp = spacy.load('en_core_web_md')
trainer = EL_Model(kb=my_kb, nlp=my_nlp)
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trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=1500, devlimit=50)
print()
# STEP 7: apply the EL algorithm on the dev dataset
if apply_to_dev:
my_nlp = spacy.load('en_core_web_md')
run_el.run_el_dev(kb=my_kb, nlp=my_nlp, training_dir=TRAINING_DIR, limit=2000)
print()
# TODO coreference resolution
# add_coref()
print()
print("STOP", datetime.datetime.now())