hpoterm_mention.is_correct = True
else:
assert False
hpoterm_mention.type = line_dict["hpoterm_types"][h_idx]
assert not hpoterm_mention.type.endswith("_UNSUP")
# Skip if the word indexes overlab
if set(g_wordidxs) & set(h_wordidxs):
continue
# Skip if the mentions are too far away
gene_start = gene_mention.wordidxs[0]
hpoterm_start = hpoterm_mention.wordidxs[0]
gene_end = gene_mention.wordidxs[-1]
hpoterm_end = hpoterm_mention.wordidxs[-1]
limits = sorted(
(gene_start, hpoterm_start, gene_end, hpoterm_end))
start = limits[0]
betw_start = limits[1]
betw_end = limits[2]
if betw_end - betw_start > 50:
continue
relation = Relation(
"GENEHPOTERM", gene_mention, hpoterm_mention)
# Add features
add_features(relation, gene_mention, hpoterm_mention,
sentence)
# Supervise
supervise(relation, gene_mention, hpoterm_mention,
sentence)
# Print!
print(relation.tsv_dump())
gene_1_mention = Mention(
"GENE", line_dict["gene_1_entity"],
[sentence.words[j] for j in line_dict["gene_1_wordidxs"]])
gene_1_mention.is_correct = line_dict["gene_1_is_correct"]
gene_1_mention.type = line_dict["gene_1_type"]
gene_2_mention = Mention(
"GENE", line_dict["gene_2_entity"],
[sentence.words[j] for j in line_dict["gene_2_wordidxs"]])
gene_2_mention.is_correct = line_dict["gene_2_is_correct"]
gene_2_mention.type = line_dict["gene_2_type"]
# If the word indexes do not overlap, create the relation candidate
# TODO there may be other cases. Check with Emily.
if not set(line_dict["gene_1_wordidxs"]) & \
set(line_dict["gene_2_wordidxs"]):
relation = Relation("GENEGENE", gene_1_mention, gene_2_mention)
# Add features
add_features(relation, gene_1_mention, gene_2_mention,
sentence)
# Supervise
# One of the two mentions (or both) is labelled as False
# We do not create a copy in this case because there will
# already be an unsupervised copy built on the unsupervised
# copies of the mentions.
if gene_1_mention.is_correct is False or \
gene_2_mention.is_correct is False:
relation.is_correct = False
relation.type = "GENEGENE_SUP_F"
# TODO Check in Emily's code how to supervise as True
# Print!
print(relation.tsv_dump())
(gene_start, hpoterm_start, gene_end, hpoterm_end))
start = limits[0]
betw_start = limits[1]
betw_end = limits[2]
if betw_end - betw_start > 50:
continue
relation = Relation(
"GENEPHENO", gene_mention, hpoterm_mention)
# Supervise
supervise(relation, gene_mention, hpoterm_mention,
sentence)
if relation.is_correct:
positive_relations.append(
(gene_mention, hpoterm_mention))
# Print!
print(relation.tsv_dump())
# Create some artificial negative examples:
# for each (gene, phenotype) pair that is labelled as positive
# example, select one word w in the same sentence that (1) is not a
# gene mention candidate and (2) is not a phenotype mention
# candidate, add (gene, w) and (w, phenotype) as negative example
avail_wordidxs = (
set(line_dict["wordidxs"]) - set(hpoterm_wordidxs)) - \
set(gene_wordidxs)
avail_wordidxs = list(avail_wordidxs)
if len(avail_wordidxs) > 0:
fake_rels = []
for (gene_mention, hpoterm_mention) in positive_relations:
other_word = sentence.words[random.choice(avail_wordidxs)]
fake_gene_mention = Mention(
"FAKE_GENE", other_word.lemma, [other_word, ])
