def main(labeled, wid_title_mapping, processed_out, discarded_out, dataset, format,
resource_namespace, fact_namespace, ontology_namespace):
# Namespace prefixes for RDF serialization
RESOURCE_NS = Namespace(resource_namespace)
FACT_EXTRACTION_NS = Namespace(fact_namespace)
ONTOLOGY_NS = Namespace(ontology_namespace)
NAMESPACE_MANAGER = NamespaceManager(Graph())
NAMESPACE_MANAGER.bind('resource', RESOURCE_NS)
NAMESPACE_MANAGER.bind('fact', FACT_EXTRACTION_NS)
NAMESPACE_MANAGER.bind('ontology', ONTOLOGY_NS)
mapping = json.load(wid_title_mapping)
with codecs.open(labeled, 'rb', 'utf8') as f:
labeled = json.load(f)
processed, discarded = to_assertions(labeled, mapping, NAMESPACE_MANAGER, {
'ontology': ONTOLOGY_NS,
'resource': RESOURCE_NS,
'fact_extraction': FACT_EXTRACTION_NS,
}, outfile=dataset, format=format)
with codecs.open(processed_out, 'wb', 'utf8') as f:
f.writelines('\n'.join(processed))
with codecs.open(discarded_out, 'wb', 'utf8') as f:
f.writelines('\n'.join(discarded))
def main(classified_output, output_file, id_to_title, format):
sentences = read_sentences(classified_output)
labeled = to_labeled(sentences)
mapping = json.load(id_to_title)
processed, discarded = to_assertions(labeled, mapping, NAMESPACE_MANAGER, {
'ontology': ONTOLOGY_NS,
'resource': RESOURCE_NS,
'fact_extraction': FACT_EXTRACTION_NS,
}, output_file, format)
def main(classified_output, output_file, id_to_title, triple_scores, \
format, sentence_score, core_weight, fe_score):
"""
serializes the classification result into triples
optionally scoring sentences and/or frame elements
"""
sentences = read_sentences(classified_output)
labeled = to_labeled(sentences, fe_score)
if sentence_score != 'nothing':
for sentence in labeled:
sentence['score'] = compute_score(sentence, sentence_score, core_weight)
mapping = json.load(id_to_title)
processed, discarded = to_assertions(labeled, mapping, output_file,
triple_scores, format)
def main(classified_output, output_file, id_to_title, triple_scores, \
format, sentence_score, core_weight, fe_score):
"""
serializes the classification result into triples
optionally scoring sentences and/or frame elements
"""
sentences = read_sentences(classified_output)
labeled = to_labeled(sentences, fe_score)
if sentence_score != 'nothing':
for sentence in labeled:
sentence['score'] = compute_score(sentence, sentence_score,
core_weight)
mapping = json.load(id_to_title)
processed, discarded = to_assertions(labeled, mapping, output_file,
triple_scores, format)
def main(labeled, wid_title_mapping, scores, processed_out, discarded_out, dataset, format,
resource_namespace, fact_namespace, ontology_namespace):
"""
this script converts the labeled data produced by the unsupervised approach into
actual triples in nt format
"""
mapping = json.load(wid_title_mapping)
with codecs.open(labeled, 'rb', 'utf8') as f:
labeled = json.load(f)
processed, discarded = to_assertions(labeled, mapping, score_dataset=scores,
outfile=dataset, format=format)
with codecs.open(processed_out, 'wb', 'utf8') as f:
f.writelines('\n'.join(processed))
with codecs.open(discarded_out, 'wb', 'utf8') as f:
f.writelines('\n'.join(discarded))
def main(labeled, wid_title_mapping, scores, processed_out, discarded_out,
dataset, format, resource_namespace, fact_namespace,
ontology_namespace):
"""
this script converts the labeled data produced by the unsupervised approach into
actual triples in nt format
"""
mapping = json.load(wid_title_mapping)
with codecs.open(labeled, 'rb', 'utf8') as f:
labeled = json.load(f)
processed, discarded = to_assertions(labeled,
mapping,
score_dataset=scores,
outfile=dataset,
format=format)
with codecs.open(processed_out, 'wb', 'utf8') as f:
f.writelines('\n'.join(processed))
with codecs.open(discarded_out, 'wb', 'utf8') as f:
f.writelines('\n'.join(discarded))