def ff_hminghkm(self,
info,
fWord,
eWord,
fIndex,
eIndex,
links,
diagValues,
currentNode=None):
"""
Return translation rules rules extracted at this node encoded as features.
"""
if currentNode.data["pos"] == '_XXX_':
return {}
name = self.ff_hminghkm.func_name
features = defaultdict(int)
start_span = currentNode.span_start()
end_span = currentNode.span_start()
l = []
minf = len(info['f'])
maxf = 0
for link in links:
if link[1] >= start_span and link[1] <= end_span:
l.append((link[0], link[1] - start_span))
if link[0] < minf:
minf = link[0]
if link[0] > maxf:
maxf = link[0]
fsubset = info['f'][minf:maxf + 1]
links_subset = [(link[0] - minf, link[1]) for link in l]
if len(links_subset) > 0:
for rule in minghkm.extract(fsubset,
currentNode,
links_subset,
start_span,
hierarchical=True):
# We only care about rules with root(LHS) = currentNode
try:
ruleRoot = rule.e.data["pos"]
except:
# Probably a blank line or a bad rule?
continue
if ruleRoot != currentNode.data["pos"]:
continue
rulestr = str(rule)
rulestr = rulestr.replace(" ", "_")
features[name + '___' + rulestr] = 1
return features
def ff_hminghkm(self, info, fWord, eWord, fIndex, eIndex, links, diagValues, currentNode = None):
"""
Return translation rules rules extracted at this node encoded as features.
"""
if currentNode.data == '_XXX_':
return {}
name = self.ff_hminghkm.func_name
features = defaultdict(int)
start_span = currentNode.span_start()
end_span = currentNode.span_start()
l = [ ]
minf = len(info['f'])
maxf = 0
for link in links:
if link[1] >= start_span and link[1] <= end_span:
l.append((link[0], link[1]-start_span))
if link[0] < minf:
minf = link[0]
if link[0] > maxf:
maxf = link[0]
fsubset = info['f'][minf:maxf+1]
links_subset = [(link[0]-minf, link[1]) for link in l]
if len(links_subset) > 0:
for rule in minghkm.extract(fsubset,
currentNode,
links_subset,
start_span,
hierarchical=True):
# We only care about rules with root(LHS) = currentNode
try:
ruleRoot = rule.e.data
except:
# Probably a blank line or a bad rule?
continue
if ruleRoot != currentNode.data:
continue
rulestr = str(rule)
rulestr = rulestr.replace(" ","_")
features[name+'___'+rulestr] = 1
return features
def ff_nonlocal_hminghkm(self, info, treeNode, edge, links, srcSpan,
tgtSpan, linkedToWords, childEdges, diagValues,
treeDistValues):
"""
Fire features for every translation rule extracted at the current node.
"""
name = self.ff_nonlocal_hminghkm.func_name
if len(links) == 0:
return {}
features = defaultdict(int)
start_span = treeNode.span_start()
end_span = treeNode.span_end()
l = []
minf = len(info['f'])
maxf = 0
for link in links:
if link[1] >= start_span and link[1] <= end_span:
l.append((link[0], link[1] - start_span))
if link[0] < minf:
minf = link[0]
if link[0] > maxf:
maxf = link[0]
fsubset = info['f'][minf:maxf + 1]
links_subset = [(link[0] - minf, link[1]) for link in l]
if len(links_subset) > 0:
for rule in minghkm.extract(fsubset,
treeNode,
links_subset,
start_span,
hierarchical=True):
try:
ruleRoot = rule.e.data["pos"]
except:
# Probably a blank line or a bad rule?
continue
rulestr = str(rule)
rulestr = rulestr.replace(" ", "_")
features[name + '___' + rulestr] = 1
return features
def ff_nonlocal_hminghkm(self, info, treeNode, edge, links, srcSpan, tgtSpan, linkedToWords, childEdges, diagValues, treeDistValues):
"""
Fire features for every translation rule extracted at the current node.
"""
name = self.ff_nonlocal_hminghkm.func_name
if len(links) == 0:
return {}
features = defaultdict(int)
start_span = treeNode.span_start()
end_span = treeNode.span_end()
l = [ ]
minf = len(info['f'])
maxf = 0
for link in links:
if link[1] >= start_span and link[1] <= end_span:
l.append((link[0], link[1]-start_span))
if link[0] < minf:
minf = link[0]
if link[0] > maxf:
maxf = link[0]
fsubset = info['f'][minf:maxf+1]
links_subset = [(link[0]-minf, link[1]) for link in l]
if len(links_subset) > 0:
for rule in minghkm.extract(fsubset, treeNode, links_subset, start_span, hierarchical=True):
try:
ruleRoot = rule.e.data
except:
# Probably a blank line or a bad rule?
continue
rulestr = str(rule)
rulestr = rulestr.replace(" ","_")
features[name+'___'+rulestr] = 1
return features
