spaCy/examples/pipeline/wiki_entity_linking/run_el.py

138 lines
4.3 KiB
Python

# coding: utf-8
from __future__ import unicode_literals
import os
import spacy
import datetime
from os import listdir
from examples.pipeline.wiki_entity_linking import training_set_creator
# requires: pip install neuralcoref --no-binary neuralcoref
# import neuralcoref
def run_el_toy_example(nlp, kb):
_prepare_pipeline(nlp, kb)
candidates = kb.get_candidates("Bush")
print("generating candidates for 'Bush' :")
for c in candidates:
print(" ", c.prior_prob, c.alias_, "-->", c.entity_ + " (freq=" + str(c.entity_freq) + ")")
print()
text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
"Douglas reminds us to always bring our towel. " \
"The main character in Doug's novel is the man Arthur Dent, " \
"but Douglas doesn't write about George Washington or Homer Simpson."
doc = nlp(text)
for ent in doc.ents:
print("ent", ent.text, ent.label_, ent.kb_id_)
def run_el_dev(nlp, kb, training_dir, limit=None):
_prepare_pipeline(nlp, kb)
correct_entries_per_article, _ = training_set_creator.read_training_entities(training_output=training_dir,
collect_correct=True,
collect_incorrect=False)
predictions = list()
golds = list()
cnt = 0
for f in listdir(training_dir):
if not limit or cnt < limit:
if is_dev(f):
article_id = f.replace(".txt", "")
if cnt % 500 == 0:
print(datetime.datetime.now(), "processed", cnt, "files in the dev dataset")
cnt += 1
with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file:
text = file.read()
doc = nlp(text)
for ent in doc.ents:
if ent.label_ == "PERSON": # TODO: expand to other types
gold_entity = correct_entries_per_article[article_id].get(ent.text, None)
# only evaluating gold entities we know, because the training data is not complete
if gold_entity:
predictions.append(ent.kb_id_)
golds.append(gold_entity)
print("Processed", cnt, "dev articles")
print()
evaluate(predictions, golds)
def is_dev(file_name):
return file_name.endswith("3.txt")
def evaluate(predictions, golds):
if len(predictions) != len(golds):
raise ValueError("predictions and gold entities should have the same length")
print("Evaluating", len(golds), "entities")
tp = 0
fp = 0
fn = 0
for pred, gold in zip(predictions, golds):
is_correct = pred == gold
if not pred:
fn += 1
elif is_correct:
tp += 1
else:
fp += 1
print("tp", tp)
print("fp", fp)
print("fn", fn)
precision = tp / (tp + fp + 0.0000001)
recall = tp / (tp + fn + 0.0000001)
fscore = 2 * recall * precision / (recall + precision + 0.0000001)
print("precision", round(100 * precision, 1), "%")
print("recall", round(100 * recall, 1), "%")
print("Fscore", round(100 * fscore, 1), "%")
def _prepare_pipeline(nlp, kb):
# TODO: the vocab objects are now different between nlp and kb - will be fixed when KB is written as part of NLP IO
el_pipe = nlp.create_pipe(name='entity_linker', config={"kb": kb})
nlp.add_pipe(el_pipe, last=True)
# TODO
def add_coref():
""" Add coreference resolution to our model """
nlp = spacy.load('en_core_web_sm')
# nlp = spacy.load('en')
# TODO: this doesn't work yet
# neuralcoref.add_to_pipe(nlp)
print("done adding to pipe")
doc = nlp(u'My sister has a dog. She loves him.')
print("done doc")
print(doc._.has_coref)
print(doc._.coref_clusters)
# TODO
def _run_ner_depr(nlp, clean_text, article_dict):
doc = nlp(clean_text)
for ent in doc.ents:
if ent.label_ == "PERSON": # TODO: expand to non-persons
ent_id = article_dict.get(ent.text)
if ent_id:
print(" -", ent.text, ent.label_, ent_id)
else:
print(" -", ent.text, ent.label_, '???') # TODO: investigate these cases