spaCy/examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py

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# coding: utf-8
from __future__ import unicode_literals
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import random
from spacy.util import minibatch, compounding
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/'
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MAX_CANDIDATES = 10
MIN_PAIR_OCC = 5
DOC_CHAR_CUTOFF = 300
EPOCHS = 5
DROPOUT = 0.1
if __name__ == "__main__":
print("START", datetime.datetime.now())
print()
nlp = spacy.load('en_core_web_lg')
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
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to_test_kb = False
# create training dataset
create_wp_training = False
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train_pipe = True
# run EL training
run_el_training = False
# apply named entity linking to the dev dataset
apply_to_dev = False
to_test_pipeline = 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_kb = kb_creator.create_kb(nlp,
max_entities_per_alias=MAX_CANDIDATES,
min_occ=MIN_PAIR_OCC,
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)
nlp.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, entity_vector_length=64) # TODO entity vectors
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:
run_el.run_kb_toy_example(kb=my_kb)
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)
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# STEP 6: create the entity linking pipe
if train_pipe:
id_to_descr = kb_creator._get_id_to_description(ENTITY_DESCR)
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train_data = training_set_creator.read_training(nlp=nlp,
training_dir=TRAINING_DIR,
id_to_descr=id_to_descr,
doc_cutoff=DOC_CHAR_CUTOFF,
dev=False,
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limit=100,
to_print=False)
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el_pipe = nlp.create_pipe(name='entity_linker', config={"kb": my_kb})
nlp.add_pipe(el_pipe, last=True)
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "entity_linker"]
with nlp.disable_pipes(*other_pipes): # only train Entity Linking
nlp.begin_training()
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for itn in range(EPOCHS):
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print()
print("EPOCH", itn)
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random.shuffle(train_data)
losses = {}
batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
docs, golds = zip(*batch)
nlp.update(
docs,
golds,
drop=DROPOUT,
losses=losses,
)
print("Losses", losses)
# STEP 7: apply the EL algorithm on the dev dataset (TODO: overlaps with code from run_el_training ?)
if apply_to_dev:
run_el.run_el_dev(kb=my_kb, nlp=nlp, training_dir=TRAINING_DIR, limit=2000)
print()
# test KB
if to_test_pipeline:
run_el.run_el_toy_example(kb=my_kb, nlp=nlp)
print()
# TODO coreference resolution
# add_coref()
print()
print("STOP", datetime.datetime.now())