def create_dataset(filename):
dataset = convert_lib.Dataset(PRECO)
lines = get_lines_from_file(filename)
for line in tqdm.tqdm(lines):
orig_document = json.loads(line)
new_document = convert_lib.Document(
convert_lib.make_doc_id(PRECO, orig_document["id"]),
DUMMY_DOC_PART)
sentence_offsets = []
token_count = 0
new_sentences, sentence_index_map, sentence_offsets = condense_sentences(
orig_document["sentences"])
new_document.sentences = new_sentences
new_document.speakers = make_empty_speakers(new_document.sentences)
new_document.clusters = []
for cluster in orig_document["mention_clusters"]:
new_cluster = []
for sentence, begin, end in cluster:
modified_sentence = sentence_index_map[sentence]
new_cluster.append([
sentence_offsets[modified_sentence] + begin,
sentence_offsets[modified_sentence] + end - 1
])
new_document.clusters.append(new_cluster)
dataset.documents.append(new_document)
return dataset
def create_dataset(filename):
dataset_name = convert_lib.DatasetName.wikicoref
dataset = convert_lib.Dataset(dataset_name)
document_counter = 0
sentence_offset = 0
curr_doc = None
curr_sent = []
curr_sent_orig_coref_labels = []
all_spans = collections.defaultdict(list)
print(filename)
for line in get_lines_from_file(filename):
if line.startswith("#end") or line.startswith("null"):
continue
elif line.startswith("#begin"):
if curr_doc is not None:
curr_doc.clusters = list(all_spans.values())
all_spans = collections.defaultdict(list)
dataset.documents.append(curr_doc)
print(line.split()[2:])
curr_doc_id = convert_lib.make_doc_id(dataset_name,
"-".join(line.split()[2:]))
curr_doc = convert_lib.Document(curr_doc_id, DUMMY_DOC_PART)
sentence_offset = 0
else:
fields = line.split()
if not fields:
if curr_sent:
add_sentence(curr_doc, curr_sent)
coref_spans = conll_lib.coref_to_spans(
curr_sent_orig_coref_labels, sentence_offset)
all_spans = ldd_append(all_spans, coref_spans)
sentence_offset += len(curr_sent)
curr_sent = []
curr_sent_orig_coref_labels = []
else:
word = fields[3]
coref_label = fields[4]
curr_sent_orig_coref_labels.append(coref_label)
curr_sent.append((word, convert_lib.NO_SPEAKER))
curr_doc.clusters = list(all_spans.values())
all_spans = collections.defaultdict(list)
dataset.documents.append(curr_doc)
return dataset
def create_dataset(filename):
dataset = convert_lib.Dataset(GAP)
with open(filename, 'r') as tsvfile:
for row in csv.DictReader(tsvfile, delimiter='\t'):
text = clean_up_text(row)
curr_document = convert_lib.Document(
convert_lib.make_doc_id(GAP, row["ID"]), DUMMY_DOC_PART)
(pronoun_indices, a_indices, b_indices) = char_to_tok_idx(
text,
((row["Pronoun"], row["Pronoun-offset"]),
(row["A"], row["A-offset"]), (row["B"], row["B-offset"])))
true_cluster = [pronoun_indices]
other_cluster = []
if row["A-coref"] == "TRUE":
true_cluster.append(a_indices)
else:
other_cluster.append(a_indices)
if row["B-coref"] == "TRUE":
true_cluster.append(b_indices)
else:
other_cluster.append(b_indices)
curr_document.sentences = [
word_tokenize(sent) for sent in sent_tokenize(text)
]
tok_running_count = 0
char_running_count = 0
for i, sent in enumerate(curr_document.sentences):
for j, tok in enumerate(sent):
char_running_count += len(tok) + 1
tok_running_count += len(sent)
curr_document.speakers = convert_lib.make_empty_speakers(
curr_document.sentences)
curr_document.clusters = [true_cluster, other_cluster]
dataset.documents.append(curr_document)
return dataset
def create_dataset(filename):
mentions_map = collections.defaultdict(list)
dataset = convert_lib.Dataset(CONLL12)
document_counter = 0
sentence_offset = 0
curr_doc = None
curr_doc_name = None
curr_sent_orig_labels = []
all_spans = collections.defaultdict(list)
sentence_idx = 0
for line in get_lines_from_file(filename):
if line.startswith("#"):
continue
if not line.strip():
# add sentence
if curr_sent_orig_labels:
(parts, tokens, pos, parse, speakers, ner,
coref) = zip(*curr_sent_orig_labels)
coref_spans = conll_lib.coref_to_spans(coref, 0)
parse_spans = conll_lib.parse_to_spans(parse)
for entity, cluster in coref_spans.items():
for start, inclusive_end in cluster:
assert len(set(parts)) == 1
assert len(set(speakers)) == 1
end = inclusive_end + 1
parse_label = parse_spans.get(
(start, inclusive_end),
"~" + "".join(parse[start:end]))
mention_obj = Mention(curr_doc_id, parts[0], entity,
sentence_idx, start, parse_label,
tokens[start:end],
pos[start:end], ner[start:end],
speakers[0])
mentions_map[mention_obj.mention_id].append(
mention_obj)
sentence_idx += 1
curr_sent = []
curr_sent_orig_labels = []
else:
fields = line.split()
# check for new doc
doc_name, part = line.split()[:2]
doc_name = doc_name.replace("/", "-")
if not doc_name == curr_doc_name:
if curr_doc is not None:
curr_doc.clusters = list(all_spans.values())
all_spans = collections.defaultdict(list)
dataset.documents.append(curr_doc)
curr_doc_name = doc_name
curr_doc_id = convert_lib.make_doc_id(CONLL12, doc_name)
curr_doc = convert_lib.Document(curr_doc_id, part)
sentence_idx = 0
sentence_offset = 0
doc, part, token_idx, token, pos, parse, _, _, _, speaker, ner = fields[:
11]
coref = fields[-1]
curr_sent_orig_labels.append(
(part, token, pos, parse, speaker, ner, coref))
for mention_id, mentions in mentions_map.items():
for mention in mentions:
print(str(mention))