def tsv_dump(self):
is_correct_str = "\\N"
if self.is_correct is not None:
is_correct_str = self.is_correct.__repr__()
tsv_line = "\t".join(
["\\N", self.doc_id, str(self.sent_id),
list2TSVarray(self.wordidxs), self.id(), self.type,
self.entity, list2TSVarray(self.words, quote=True),
is_correct_str, list2TSVarray(list(self.features), True)])
return tsv_line
def tsv_dump(self):
is_correct_str = "\\N"
if self.is_correct is not None:
is_correct_str = self.is_correct.__repr__()
tsv_line = "\t".join([
"\\N", self.doc_id,
str(self.sent_id),
list2TSVarray(self.wordidxs),
self.id(), self.type, self.entity,
list2TSVarray(self.words, quote=True), is_correct_str
])
return tsv_line
def tsv_dump(self):
is_correct_str = "\\N"
if self.is_correct is not None:
is_correct_str = self.is_correct.__repr__()
return "\t".join(
["\\N", self.doc_id, str(self.sent_id_1), str(self.sent_id_2),
self.id(), self.type, self.mention_1_id, self.mention_2_id,
list2TSVarray([x.in_sent_idx for x in self.mention_1_words]),
list2TSVarray([x.in_sent_idx for x in self.mention_2_words]),
list2TSVarray([x.word for x in self.mention_1_words], True),
list2TSVarray([x.word for x in self.mention_2_words], True),
is_correct_str])
def tsv_dump(self):
is_correct_str = "\\N"
if self.is_correct is not None:
is_correct_str = self.is_correct.__repr__()
return "\t".join([
"\\N", self.doc_id,
str(self.sent_id_1),
str(self.sent_id_2),
self.id(), self.type, self.mention_1_id, self.mention_2_id,
list2TSVarray([x.in_sent_idx for x in self.mention_1_words]),
list2TSVarray([x.in_sent_idx for x in self.mention_2_words]),
list2TSVarray([x.word for x in self.mention_1_words], True),
list2TSVarray([x.word for x in self.mention_2_words], True),
is_correct_str
])
def process_files(proc_id, input_files, input_dir, output_dir, mode):
with open(
os.path.realpath("{}/sentences-{}.{}".format(
output_dir, proc_id, mode)), 'wt') as out_file:
for filename in input_files:
# Docid assumed to be the filename.
docid = filename
with open(os.path.realpath(input_dir + "/" + filename),
'rt') as curr_file:
atEOF = False
# Check if the file is empty (we are at End of File)
curr_pos = curr_file.tell()
curr_file.read(1)
new_pos = curr_file.tell()
if new_pos == curr_pos:
atEOF = True
else:
curr_file.seek(curr_pos)
# One iteration of the following loop corresponds to one sentence
while not atEOF:
sent_id = -1
wordidxs = []
words = []
poses = []
ners = []
lemmas = []
dep_paths = []
dep_parents = []
bounding_boxes = []
curr_line = curr_file.readline().strip()
# Sentences are separated by empty lines in the parser output file
while curr_line != "":
tokens = curr_line.split("\t")
if len(tokens) != 9:
sys.stderr.write(
"ERROR: malformed line (wrong number of fields): {}\n"
.format(curr_line))
return 1
word_idx, word, pos, ner, lemma, dep_path, dep_parent, word_sent_id, bounding_box = tokens
# Normalize sentence id
word_sent_id = int(word_sent_id.replace("SENT_", ""))
# assign sentence id if this is the first word of the sentence
if sent_id == -1:
sent_id = word_sent_id
# sanity check for word_sent_id
elif sent_id != word_sent_id:
sys.stderr.write(
"ERROR: found word with mismatching sent_id w.r.t. sentence: {} != {}\n"
.format(word_sent_id, sent_id))
return 1
# Normalize bounding box, stripping initial '[' and
# final '],' and concatenating components
bounding_box = bounding_box[1:-2]
bounding_box = bounding_box.replace(", ", "-")
# Append contents of this line to the sentence arrays
wordidxs.append(int(word_idx) - 1) # Start from 0
words.append(word)
poses.append(pos)
ners.append(ner)
lemmas.append(lemma)
dep_paths.append(dep_path)
# Now "-1" means root and the rest correspond to array indices
dep_parents.append(int(dep_parent) - 1)
bounding_boxes.append(bounding_box)
# Read the next line
curr_line = curr_file.readline().strip()
# Write sentence to output
if mode == "tsv":
out_file.write("{}\n".format("\t".join([
docid,
str(sent_id),
list2TSVarray(wordidxs),
list2TSVarray(words, quote=True),
list2TSVarray(poses, quote=True),
list2TSVarray(ners),
list2TSVarray(lemmas, quote=True),
list2TSVarray(dep_paths, quote=True),
list2TSVarray(dep_parents),
list2TSVarray(bounding_boxes)
])))
elif mode == "json":
out_file.write("{}\n".format(
json.dumps({
"doc_id": docid,
"sent_id": sent_id,
"wordidxs": wordidxs,
"words": words,
"poses": poses,
"ners": ners,
"lemmas": lemmas,
"dep_paths": dep_paths,
"dep_parents": dep_parents,
"bounding_boxes": bounding_boxes
})))
# Check if we are at End of File
curr_pos = curr_file.tell()
curr_file.read(1)
new_pos = curr_file.tell()
if new_pos == curr_pos:
atEOF = True
else:
curr_file.seek(curr_pos)
doc_id = line_dict["doc_id"]
sent_id = line_dict["sent_id"]
words = line_dict["words"]
wordidxs = [x for x in range(len(words))]
poses = line_dict["poses"]
ners = line_dict["ners"]
lemmas = line_dict["lemmas"]
dep_paths_orig = line_dict["dep_paths"]
bounding_boxes = ["empty"] * len(words)
gene_index = int(doc_id.split("-")[-1])
# Compute dependency path edge labels and node parents
dep_paths = ["_"] * len(words)
dep_parents = [0] * len(words)
for dep_path in dep_paths_orig:
tokens = dep_path.split("(")
dep_parent = int((tokens[1].split(", ")[0]).split("-")[-1]) - 1
dep_child = int((tokens[1].split(", ")[-1]).split("-")[-1][:-1]) - 1
dep_paths[dep_child] = tokens[0]
dep_parents[dep_child] = dep_parent
print("{}".format("\t".join([doc_id, str(sent_id),
list2TSVarray(wordidxs), list2TSVarray(words,
quote=True), list2TSVarray(poses, quote=True),
list2TSVarray(ners), list2TSVarray(lemmas, quote=True),
list2TSVarray(dep_paths, quote=True),
list2TSVarray(dep_parents),
list2TSVarray(bounding_boxes), genes[gene_index]])))
# Check if the definition contains some keywords that
# make us suspect that it is probably a gene/protein.
# This list is incomplete, and it would be good to add
# to it.
if contains_kw:
continue
for word in definition.split():
if word.endswith("ase") and len(word) > 5:
contains_kw = True
break
if " gene" in definition or \
"protein" in definition or \
"factor" in definition or \
"ligand" in definition or \
"enzyme" in definition or \
"receptor" in definition or \
"pseudogene" in definition:
contains_kw = True
# If no significant keyword in any definition, supervise as not
# correct
if not contains_kw and not is_correct:
is_correct = False
is_correct_str = "\\N"
if is_correct is not None:
is_correct_str = is_correct.__repr__()
print("\t".join(
(line_dict["doc_id"], acronym,
list2TSVarray(list(acronyms[acronym]), quote=True),
is_correct_str)))
break
else:
# Check if the definition contains some keywords that
# make us suspect that it is probably a gene/protein.
# This list is incomplete, and it would be good to add
# to it.
if contains_kw:
continue
for word in definition.split():
if word.endswith("ase") and len(word) > 5:
contains_kw = True
break
if " gene" in definition or \
"protein" in definition or \
"factor" in definition or \
"ligand" in definition or \
"enzyme" in definition or \
"receptor" in definition or \
"pseudogene" in definition:
contains_kw = True
# If no significant keyword in any definition, supervise as not
# correct
if not contains_kw and not is_correct:
is_correct = False
is_correct_str = "\\N"
if is_correct is not None:
is_correct_str = is_correct.__repr__()
print("\t".join((line_dict["doc_id"], acronym,
list2TSVarray(list(acronyms[acronym]),
quote=True), is_correct_str)))
def process_files(proc_id, input_files, input_dir, output_dir, mode):
with open(os.path.realpath("{}/sentences-{}.{}".format(output_dir, proc_id, mode)), 'wt') as out_file:
for filename in input_files:
# Docid assumed to be the filename.
docid = filename
with open(os.path.realpath(input_dir + "/" + filename), 'rt') as curr_file:
atEOF = False
# Check if the file is empty (we are at End of File)
curr_pos = curr_file.tell()
curr_file.read(1)
new_pos = curr_file.tell()
if new_pos == curr_pos:
atEOF = True
else:
curr_file.seek(curr_pos)
# One iteration of the following loop corresponds to one sentence
while not atEOF:
sent_id = -1
wordidxs = []
words = []
poses = []
ners = []
lemmas = []
dep_paths = []
dep_parents = []
bounding_boxes = []
curr_line = curr_file.readline().strip()
# Sentences are separated by empty lines in the parser output file
while curr_line != "":
tokens = curr_line.split("\t")
if len(tokens) != 9:
sys.stderr.write("ERROR: malformed line (wrong number of fields): {}\n".format(curr_line))
return 1
word_idx, word, pos, ner, lemma, dep_path, dep_parent, word_sent_id, bounding_box = tokens
# Normalize sentence id
word_sent_id = int(word_sent_id.replace("SENT_", ""))
# assign sentence id if this is the first word of the sentence
if sent_id == -1:
sent_id = word_sent_id
# sanity check for word_sent_id
elif sent_id != word_sent_id:
sys.stderr.write("ERROR: found word with mismatching sent_id w.r.t. sentence: {} != {}\n".format(word_sent_id, sent_id))
return 1
# Normalize bounding box, stripping initial '[' and
# final '],' and concatenating components
bounding_box = bounding_box[1:-2]
bounding_box = bounding_box.replace(", ", "-")
# Append contents of this line to the sentence arrays
wordidxs.append(int(word_idx) - 1) # Start from 0
words.append(word)
poses.append(pos)
ners.append(ner)
lemmas.append(lemma)
dep_paths.append(dep_path)
# Now "-1" means root and the rest correspond to array indices
dep_parents.append(int(dep_parent) - 1)
bounding_boxes.append(bounding_box)
# Read the next line
curr_line = curr_file.readline().strip()
# Write sentence to output
if mode == "tsv":
out_file.write("{}\n".format("\t".join([docid, str(sent_id),
list2TSVarray(wordidxs), list2TSVarray(words,
quote=True), list2TSVarray(poses, quote=True),
list2TSVarray(ners), list2TSVarray(lemmas, quote=True),
list2TSVarray(dep_paths, quote=True),
list2TSVarray(dep_parents),
list2TSVarray(bounding_boxes)])))
elif mode == "json":
out_file.write("{}\n".format(json.dumps({ "doc_id": docid, "sent_id": sent_id,
"wordidxs": wordidxs, "words": words, "poses": poses,
"ners": ners, "lemmas": lemmas, "dep_paths": dep_paths,
"dep_parents": dep_parents, "bounding_boxes":
bounding_boxes})))
# Check if we are at End of File
curr_pos = curr_file.tell()
curr_file.read(1)
new_pos = curr_file.tell()
if new_pos == curr_pos:
atEOF = True
else:
curr_file.seek(curr_pos)