def predict_entities(self, collection):
next_id = 0
for instance_keyphrase, label in self.keyphrases.items():
for sentence in collection.sentences:
text = sentence.text.lower()
pattern = r'\b' + instance_keyphrase + r'\b'
for match in re.finditer(pattern, text):
keyphrase = Keyphrase(sentence, label, next_id, [match.span()])
keyphrase.split()
next_id += 1
sentence.keyphrases.append(keyphrase)
def load_keyphrases(cls, collection: Collection, finput: Path):
cls.load_input(collection, finput)
input_a_file = finput.parent / ("output_a_" + finput.name.split("_")[1])
sentences_length = [len(s.text) for s in collection.sentences]
for i in range(1, len(sentences_length)):
sentences_length[i] += sentences_length[i - 1] + 1
sentence_by_id = {}
for line in input_a_file.open(encoding="utf8").readlines():
lid, spans, label, _ = line.strip().split("\t")
lid = int(lid)
spans = [s.split() for s in spans.split(";")]
spans = [(int(start), int(end)) for start, end in spans]
# find the sentence where this annotation is
i = bisect.bisect(sentences_length, spans[0][0])
# correct the annotation spans
if i > 0:
spans = [
(
start - sentences_length[i - 1] - 1,
end - sentences_length[i - 1] - 1,
)
for start, end in spans
]
spans.sort(key=lambda t: t[0])
# store the annotation in the corresponding sentence
the_sentence = collection.sentences[i]
keyphrase = Keyphrase(the_sentence, label, lid, spans)
the_sentence.keyphrases.append(keyphrase)
if len(keyphrase.spans) == 1:
keyphrase.split()
sentence_by_id[lid] = the_sentence
return sentence_by_id
def run(self, collection, taskA, taskB):
gold_keyphrases, gold_relations = self.model
if taskA:
next_id = 0
for gold_keyphrase, label in gold_keyphrases.items():
for sentence in collection.sentences:
text = sentence.text.lower()
pattern = r"\b" + gold_keyphrase + r"\b"
for match in re.finditer(pattern, text):
keyphrase = Keyphrase(sentence, label, next_id,
[match.span()])
keyphrase.split()
next_id += 1
sentence.keyphrases.append(keyphrase)
if taskB:
for sentence in collection.sentences:
for origin in sentence.keyphrases:
origin_text = origin.text.lower()
for destination in sentence.keyphrases:
destination_text = destination.text.lower()
try:
label = gold_relations[origin_text, origin.label,
destination_text,
destination.label, ]
except KeyError:
continue
relation = Relation(sentence, origin.id,
destination.id, label)
sentence.relations.append(relation)
sentence.remove_dup_relations()
return collection