def genNETypeWordConcept(self, files, datasets, common_dataset,
default_dataset):
g = input.MultiReader(files, input.DXMLReader)
g = input.InputGenerator(g, datasets, default_dataset)
g = self.generalizer(g, common_dataset)
for da in g:
type_counts = ADict()
ne_typed = [(ne_type, ) + (g, ) + w for ne_type, g, w in zip(
da['ne_typed'], da['generalized'], da['requested'])]
filtered = self.filterNEtext(ne_typed)
for item in filtered:
ne_type = item[0]
if ne_type is not None:
type_counts[ne_type.upper()] += 1
semantics = da.get('semantics', '')
tree = OrderedTree.fromString(semantics)
tree_counts = tree.getConceptCounts()
bad_ne = set()
for concept, count in type_counts.iteritems():
if tree_counts.get(concept, 0) != count:
bad_ne.add(concept)
ne_types = set(type_counts.keys()) - set(bad_ne)
if not bad_ne:
splits = tree.splitStateVector(*list(ne_types))
for (ne_type, text), states in zip(filtered, splits):
yield ne_type, text, states
else:
# Some conflicts or no named entities
states = tree.toStateVector()
for (ne_type, text) in filtered:
if ne_type in tree.conceptCounts:
only_states = [i for i in states if ne_type in i]
yield ne_type, text, only_states
else:
yield ne_type, text, states
def genNETypeWordConcept(self, files, datasets, common_dataset, default_dataset):
g = input.MultiReader(files, input.DXMLReader)
g = input.InputGenerator(g, datasets, default_dataset)
g = self.generalizer(g, common_dataset)
for da in g:
type_counts = ADict()
ne_typed = [(ne_type,)+(g,)+w for ne_type, g, w in
zip(da['ne_typed'], da['generalized'], da['requested'])]
filtered = self.filterNEtext(ne_typed)
for item in filtered:
ne_type = item[0]
if ne_type is not None:
type_counts[ne_type.upper()] += 1
semantics = da.get('semantics', '')
tree = OrderedTree.fromString(semantics)
tree_counts = tree.getConceptCounts()
bad_ne = set()
for concept, count in type_counts.iteritems():
if tree_counts.get(concept, 0) != count:
bad_ne.add(concept)
ne_types = set(type_counts.keys()) - set(bad_ne)
if not bad_ne:
splits = tree.splitStateVector(*list(ne_types))
for (ne_type, text), states in zip(filtered, splits):
yield ne_type, text, states
else:
# Some conflicts or no named entities
states = tree.toStateVector()
for (ne_type, text) in filtered:
if ne_type in tree.conceptCounts:
only_states = [i for i in states if ne_type in i]
yield ne_type, text, only_states
else:
yield ne_type, text, states
def flushLine(self):
self.removeSeparator()
ret = ''.join(self)
del self[:]
return OrderedTree.fromString(ret, label=ROOT_CONCEPT)
def mapFromMLF(cls, contents):
str = ' '.join(s.strip() for s in contents)
return OrderedTree.fromString(str, label=ROOT_CONCEPT)
