def can_apply(self, configuration: Configuration, transition: str) -> bool:
"""
Determine whether the given transition is legal for this
configuration.
"""
if transition.startswith("L") or transition.startswith("R"):
label = transition[2:-1]
if transition.startswith("L"):
h = configuration.get_stack(0)
else:
h = configuration.get_stack(1)
if h < 0:
return False
if h == 0 and label != self.root_label:
return False
n_stack = configuration.get_stack_size()
n_buffer = configuration.get_buffer_size()
if transition.startswith("L"):
return n_stack > 2
elif transition.startswith("R"):
if self.single_root:
return (n_stack > 2) or (n_stack == 2 and n_buffer == 0)
else:
return n_stack >= 2
return n_buffer > 0
def get_configuration_features(configuration: Configuration,
vocabulary: Vocabulary) -> List[List[int]]:
"""
=================================================================
Implement feature extraction described in
"A Fast and Accurate Dependency Parser using Neural Networks"(2014)
=================================================================
"""
# TODO(Students) Start
stack_size = configuration.get_stack_size()
buffer_size = configuration.get_buffer_size()
#print("Stack:"+str(stack_size))
#print("Buffer"+str(buffer_size))
stack_words = []
buffer_words = []
fst_2nd_leftmost_rightmost = []
fst_2nd_leftmost_leftmost_rightmost_rightmost = []
for i in range(0,3) :
stack_words.append(configuration.get_stack(i))
buffer_words.append(configuration.get_buffer(i))
if i < 2 :
fst_2nd_leftmost_rightmost.append(configuration.get_left_child(stack_words[i],1))
fst_2nd_leftmost_rightmost.append(configuration.get_right_child(stack_words[i], 1))
fst_2nd_leftmost_rightmost.append(configuration.get_left_child(stack_words[i], 2))
fst_2nd_leftmost_rightmost.append(configuration.get_right_child(stack_words[i], 2))
fst_2nd_leftmost_rightmost.append( configuration.get_left_child(configuration.get_left_child(stack_words[i],1),1) )
fst_2nd_leftmost_rightmost.append( configuration.get_right_child(configuration.get_right_child(stack_words[i], 1),1) )
#fst_2nd_leftmost_leftmost_rightmost_rightmost.append(
#configuration.get_left_child(configuration.get_left_child(stack_words[i],1),1) )
#fst_2nd_leftmost_leftmost_rightmost_rightmost.append(
#configuration.get_right_child(configuration.get_right_child(stack_words[i], 1),1) )
final = stack_words + buffer_words + fst_2nd_leftmost_rightmost #+ fst_2nd_leftmost_leftmost_rightmost_rightmost
pos_tags = []
for j in range(0,len(final)) :
pos_tags.append(vocabulary.get_pos_id(configuration.get_pos(final[j])))
temp_final = final[6:18]
arc_labels = []
for j in range(0,len(temp_final)) :
arc_labels.append(vocabulary.get_label_id(configuration.get_label(temp_final[j])))
for i,x in enumerate(final) :
final[i] = vocabulary.get_word_id(configuration.get_word(final[i]))
features = final + pos_tags + arc_labels
# TODO(Students) End
assert len(features) == 48
return features
def is_terminal(self, configuration: Configuration) -> bool:
return configuration.get_stack_size(
) == 1 and configuration.get_buffer_size() == 0
