def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
# TODO: Write the body of this loop for part 4
input_vec = self.extractor.get_input_representation(
words, pos, state)
output_vec = self.model.predict(input_vec.reshape((1, 6)))[0]
sortedIdx_by_possibility = np.argsort(output_vec)[::-1]
permitted_idx = 0
permitted_action, rel = self.output_labels[
sortedIdx_by_possibility[permitted_idx]]
while (len(state.stack) == 0 and permitted_action in {'left_arc','right_arc'}) \
or (len(state.buffer) == 1 and permitted_action=='shift' and len(state.stack) > 0) \
or (len(state.stack) > 0 and state.stack[-1] == 0 and permitted_action == 'left_arc'):
permitted_idx += 1
permitted_action, rel = self.output_labels[
sortedIdx_by_possibility[permitted_idx]]
if permitted_action == 'shift':
state.shift()
elif permitted_action == 'left_arc':
state.left_arc(rel)
elif permitted_action == 'right_arc':
state.right_arc(rel)
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1,len(words)))
state.stack.append(0)
while state.buffer:
# TODO: Write the body of this loop for part 4
vecc = self.extractor.get_input_representation(words, pos, state)
possible = list(self.model.predict(vecc)[0])
#informed by "https://stackoverflow.com/questions/4233476/sort-a-list-by-multiple-attributes"
sorted_list = [j[0] for j in sorted(enumerate(possible), reverse=True, key=lambda x:x[1])]
i=0
t=self.output_labels[sorted_list[i]][0]
while ((len(state.stack) == 0 and t in {"right_arc", "left_arc"})
or (len(state.stack) > 0 and len(state.buffer) == 1 and t == "shift")
or (len(state.stack) > 0 and state.stack[-1] == 0 and t == "left_arc")):
i+=1
t=self.output_labels[sorted_list[i]][0]
#retreives dependency structure
if self.output_labels[sorted_list[i]][1] == None:
state.shift()
else:
if self.output_labels[sorted_list[i]][0] == "left_arc":
state.left_arc(self.output_labels[sorted_list[i]][1])
elif self.output_labels[sorted_list[i]][0] == "right_arc":
state.right_arc(self.output_labels[sorted_list[i]][1])
result = DependencyStructure()
for p,c,r in state.deps:
result.add_deprel(DependencyEdge(c,words[c],pos[c],p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
forbit_count = 0
while state.buffer:
input = self.extractor.get_input_representation(words, pos, state)
transition_vec = self.model.predict(np.asmatrix(input)).tolist()[0]
temp = [[i, v] for i, v in enumerate(transition_vec)]
sort_temp = sorted(temp, key=lambda x: x[1], reverse=True)
forbid = set()
if len(state.stack) == 0:
forbid.add('left_arc')
forbid.add('right_arc')
if len(state.buffer) == 1 and len(state.stack) != 0:
forbid.add('shift')
if len(state.stack) > 0 and state.stack[-1] == 0:
forbid.add('left_arc')
for idx, _ in sort_temp:
transition = self.output_labels[idx]
if transition[0] not in forbid:
break
forbit_count += 1
if transition[0] == 'shift':
state.shift()
elif transition[0] == 'left_arc':
state.left_arc(transition[1])
elif transition[0] == 'right_arc':
state.right_arc(transition[1])
else:
print('error at state transition')
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
input_vec = self.extractor.get_input_representation(
words, pos, state)
input_vec = input_vec.reshape((1, 6))
possible_actions = self.model.predict(input_vec)[0].tolist()
sorted_actions = [i[0] for i in sorted(enumerate(possible_actions), \
reverse=True, key=lambda x:x[1])]
i = 0
while not self.check_action(
self.output_labels[sorted_actions[i]][0], state):
i += 1
action, dep_rel = self.output_labels[sorted_actions[i]]
if not dep_rel:
state.shift()
elif action == "left_arc":
state.left_arc(dep_rel)
elif action == "right_arc":
state.right_arc(dep_rel)
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1,len(words)))
state.stack.append(0)
while state.buffer:
# TODO: Write the body of this loop for part 4
# TODO when the buffer is empty
input_rep = self.extractor.get_input_representation(words, pos, state)
output_rep = self.model.predict(np.array( [input_rep] ))
legal_moves = list(filter(isLegal(state), np.arange(NUM_CLASSES)))
sorted_transition_index = reversed(np.argsort(output_rep)[0])
legal_transition_index = list(filter(contains(legal_moves), sorted_transition_index))
transition_index = legal_transition_index[0]
(operator, label) = toTransition(transition_index)
if operator == "left_arc":
state.left_arc(label)
if operator == "right_arc":
state.right_arc(label)
if operator == "shift":
state.shift()
result = DependencyStructure()
for p,c,r in state.deps:
result.add_deprel(DependencyEdge(c,words[c],pos[c],p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
inp = self.extractor.get_input_representation(words, pos, state)
output = self.model.predict(inp.reshape(1, -1))
"""
Arc-left or Arc-right are not permitted the stack is empty.
Shifting the only word out of the buffer is also illegal, unless the stack is empty.
Finally, the root node must never be the target of a left-arc.
"""
for idx in output[0].argsort()[::-1]:
transition, label = self.output_labels[idx]
if transition == 'shift':
if len(state.buffer) == 1 and len(state.stack) > 0:
continue
else:
if len(state.stack) == 0: continue
if transition == 'left_arc' and state.stack[-1] == 0:
continue
break
if transition == 'shift': state.shift()
elif transition == 'left_arc': state.left_arc(label)
else: state.right_arc(label)
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
# TODO: Write the body of this loop for part 4
features = self.extractor.get_input_representation(
words, pos, state).reshape((1, -1))
ans = self.model.predict(features)[0]
indexes = np.argsort(ans)[::-1][:20]
for i in indexes:
arc, dep = self.output_labels[i]
if arc == 'shift':
# shift only word out of buffer is illegal, unless stack empty
if not state.stack:
state.shift()
break
elif len(state.buffer) > 1:
state.shift()
break
elif arc == 'left_arc':
# arcs not permitted when stack empty
if state.stack and state.stack[
-1] != 0: # root node can be target of left arc
state.left_arc(dep)
break
elif arc == 'right_arc':
if state.stack:
state.right_arc(dep)
break
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
# print(words)
# print(pos)
# print(state)
# sys.exit()
it = 0
while state.buffer:
# TODO: Write the body of this loop for part 4
# Use extractor object to get input representation
input = self.extractor.get_input_representation(words, pos, state)
# IMPORTANT: Reshape to (1, len(input)) or (1,-1)
# Otherwise, an error will be yielded
# 1, in this case, is the batch_size (we feed the input one by one)
# input.resize(1, len(input))
# Get the prediction from the neural net
softmax_output = self.model.predict(input.reshape(1, -1))
# IMPORTANT: softmax_output is a 2d array of shape (1, 91).
# Thus, to get list of probabilities, take its first element
# Sort the actions using subfunction
sorted_actions = self.sort_output(softmax_output[0])
# Loop over actions, starting with the one with highest probability
for i in range(0, len(sorted_actions)):
action = sorted_actions[i][0]
# Execute the action only if it's valid given the current state
# Otherwise, try the next possible action
# Check using subfunction
if self.is_action_permitted(action, state):
# if i > -1 and i < 10:
# print('i = ' + str(i))
# Execute the action accordingly
# print(action[0])
# print(it)
if action[0] == 'shift':
state.shift()
# For left and right arc, supply the relationship type
elif action[0] == 'left_arc':
state.left_arc(action[1])
elif action[0] == 'right_arc':
state.right_arc(action[1])
# IMPORTANT: Don't forget to break the loop so that
# only one action is executed. The next action
# will be determined again by the neural net
break
it = it + 1
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
pass
# TODO: Write the body of this loop for part 4
feature = self.extractor.get_input_representation(
words, pos, state)
feature = feature.reshape(1, feature.shape[0])
score = self.model.predict(feature)
actions = {}
for i in range(len(score[0])):
if score[0][i] > 0:
actions.update({i: score[0][i]})
sort_actions = {
k: v
for k, v in sorted(
actions.items(), key=lambda item: item[1], reverse=True)
}
for a in sort_actions:
real_actions = self.output_labels[a]
if ('arc' in real_actions[0] and len(state.stack) == 0):
continue
elif (real_actions[0] == 'shift' and len(state.buffer) == 1
and len(state.stack) > 0):
continue
elif (real_actions[0] == 'left_arc' and state.stack[-1] == 0):
continue
else:
if (real_actions[0] == 'shift'):
state.shift()
break
elif (real_actions[0] == 'left_arc'):
state.left_arc(real_actions[1])
break
elif (real_actions[0] == 'right_arc'):
state.right_arc(real_actions[1])
break
else:
print("something is wroooooooong")
break
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
# pass
# TODO: Write the body of this loop for part 4
# step 1:
features = self.extractor.get_input_representation(
words, pos, state)
possible_actions = self.model.predict(features.reshape([1, 6]))
possible_actions = possible_actions.reshape(91)
# step 2: select the highest scoring permitted transition
# create a possible action indices list sorted by their possibility(largest one comes first)
# sorted_actions_indices = np.flipud(np.argsort(possible_actions))
sorted_actions_indices = np.flipud(np.argsort(possible_actions))
# going through and find the highest scoring permitted trasition
for i in sorted_actions_indices:
flag = False
# check the current transition whether permitted or not
if self.output_labels[i][0] == "shift":
if state.stack and len(state.buffer) == 1:
flag = False
else:
flag = True
elif self.output_labels[i][0] == "left_arc":
if not state.stack:
flag = False
elif state.stack[-1] == 0:
flag = False
else:
flag = True
elif self.output_labels[i][0] == "right_arc":
if not state.stack:
flag = False
else:
flag = True
# when flag == True, it states that the cuurent transition is permitted
if flag == True:
transition = self.output_labels[i]
# update the state accordingly
if transition[0] == "shift":
state.shift()
elif transition[0] == "left_arc":
state.left_arc(transition[1])
elif transition[0] == "right_arc":
state.right_arc(transition[1])
break
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
# The function first creates a State instance in the initial state, i.e. only word 0 is on the stack, the
# buffer contains all input words (or rather, their indices) and the deps structure is empty
state = State(range(1, len(words)))
state.stack.append(0)
# The algorithm is the standard transition-based algorithm. As long as the buffer is not empty, we
# use the feature extractor to obtain a representation of the current state
while state.buffer:
# TODO: Write the body of this loop for part 4
transitions = []
if not state.stack:
transitions.append('shift')
elif state.stack[-1] == 0:
transitions.append('right_arc')
if len(state.buffer) > 1:
transitions.append('shift')
else:
transitions.append('right_arc')
transitions.append('left_arc')
if len(state.buffer) > 1:
transitions.append('shift')
#print(transitions)
# As long as the buffer is not empty,
# use the feature extractor to obtain a representation of the current state
input_rep = self.extractor.get_input_representation(
words, pos, state).reshape((1, 6))
# print(input_rep)
# Call model.predict(features) and retrieve a softmax actived vector of possible actions
predict = self.model.predict(input_rep)[0]
#print(predict)
index = list(np.argsort(predict)[::-1])
#print(index)
for i in index:
action, label = self.output_labels[i]
if action in transitions:
if action == 'shift':
state.shift()
elif action == 'left_arc':
state.left_arc(label)
else:
state.right_arc(label)
break
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1,len(words)))
state.stack.append(0)
while state.buffer:
self.input_representation = self.extractor.get_input_representation(words,pos,state)
self.input_representation = np.reshape(self.input_representation,(-1,6))
self.prediction = self.model.predict(self.input_representation)
self.prediction_list = self.prediction[0].tolist()
self.output_proabilities = []
for i in range(0,len(self.prediction_list)):
self.output_proabilities.append((self.prediction_list[i],i))
self.output_proabilities.sort(reverse = True, key = lambda x: x[0])
# Check the rules for actions
for item in self.output_proabilities:
self.predicted_action = self.output_labels.get(item[1])[0]
# arc-left or arc-right are not permitted if the stack is empty.
# Finally, the root node must never be the target of a left-arc.
if self.predicted_action == 'left_arc':
if len(state.stack) == 0 or state.stack[-1] == 0:
continue
else:
state.left_arc(self.output_labels.get(item[1])[1])
break
elif self.predicted_action == 'right_arc':
if len(state.stack) == 0:
continue
else:
state.right_arc(self.output_labels.get(item[1])[1])
break
# Shifting the only word out of the buffer is also illegal, unless the stack is empty.
elif self.predicted_action == 'shift':
if len(state.buffer) == 1:
if len(state.stack) == 0:
state.shift()
break
else:
continue
else:
state.shift()
break
result = DependencyStructure()
for p,c,r in state.deps:
result.add_deprel(DependencyEdge(c,words[c],pos[c],p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
features = self.extractor.get_input_representation(
words, pos, state)
features = features.reshape(1, 6)
possible_transitions = {}
predict_probs = self.model.predict(features)[0]
for i, prob in enumerate(predict_probs):
if prob > 0.000000000e+00:
possible_transitions[i] = prob
possible_transitions = sorted(possible_transitions.items(),
key=lambda kv: -kv[1])
# print(possible_transitions)
for transitionid, prob in possible_transitions:
action = self.uniquepairs[transitionid][0]
deprel = self.uniquepairs[transitionid][1]
if action == 'shift':
if len(state.buffer) == 1 and not state.stack:
state.shift()
break
elif len(state.buffer) > 1:
state.shift()
break
elif len(possible_transitions) == 1:
state.shift()
break
elif action == 'left_arc':
if state.stack and state.stack[-1] != 0:
state.left_arc(deprel)
break
else:
if state.stack:
state.right_arc(deprel)
break
# print(state.stack, state.buffer)
# TODO: Write the body of this loop for part 4
result = DependencyStructure()
# print(state.deps)
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1,len(words)))
state.stack.append(0)
dep_relations = ['tmod', 'vmod', 'csubjpass', 'rcmod', 'ccomp', 'poss', 'parataxis', 'appos', 'dep', 'iobj', 'pobj', 'mwe', 'quantmod', 'acomp', 'number', 'csubj', 'root', 'auxpass', 'prep', 'mark', 'expl', 'cc', 'npadvmod', 'prt', 'nsubj', 'advmod', 'conj', 'advcl', 'punct', 'aux', 'pcomp', 'discourse', 'nsubjpass', 'predet', 'cop', 'possessive', 'nn', 'xcomp', 'preconj', 'num', 'amod', 'dobj', 'neg','dt','det']
while state.buffer:
#pass
# TODO: Write the body of this loop for part 4
features = self.extractor.get_input_representation(words, pos, state)
soft_acts = self.model.predict(features.reshape(-1,6))
sort_ind = [i[0] for i in sorted(enumerate(soft_acts[0]), key=lambda x:x[1], reverse = True)]
for i in sort_ind:
if i == 90:
if len(state.buffer) > 1:
state.shift()
break
elif state.stack == []:
state.shift()
break
else:
continue
elif i >= 45 and i < 90:
if state.stack == []:
continue
else:
state.right_arc(self.output_labels[i-45])
break
else:
if state.stack == []:
continue
elif len(state.stack) == 1:
continue
else:
state.left_arc(self.output_labels[i])
break
result = DependencyStructure()
for p,c,r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c],p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
# TODO: Write the body of this loop for part 4
features = self.extractor.get_input_representation(
words, pos, state).reshape((1, 6))
sorted_output = self.model.predict(features)[0].argsort()[::-1]
for i in sorted_output:
transition, label = self.output_labels[i]
if transition == 'shift':
if not state.stack:
state.shift()
elif len(state.buffer) > 1:
state.shift()
else:
continue
elif transition == 'left_arc':
if not state.stack:
continue
elif state.stack[-1] == 0:
continue
else:
state.left_arc(label)
elif transition == 'right_arc':
if not state.stack:
continue
else:
state.right_arc(label)
else:
raise ValueError('transition error')
break
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
# build parser
while state.buffer:
features = self.extractor.get_input_representation(
words, pos, state)
a = self.model.predict(features.reshape((-1, 6)))
indices = np.argsort(a)
for i in indices:
for j in reversed(i):
high = self.output_labels.get(j)
if high[0] == 'shift':
if len(state.buffer) == 1 and len(state.stack) > 0:
continue
else:
state.shift()
break
elif high[0] == 'left_arc':
if len(state.stack) == 0:
continue
elif high[1] == 'root':
continue
else:
state.left_arc(high[1])
break
elif high[0] == 'right_arc':
if len(state.stack) == 0:
continue
else:
state.right_arc(high[1])
break
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
# pass
# TODO: Write the body of this loop for part 4
feature = self.extractor.get_input_representation(
words, pos, state)
actions = self.model.predict(np.vstack([feature]))
lst = list(
zip(actions.tolist()[0], range(len(actions.tolist()[0]))))
lst.sort(reverse=True)
label = None
for probability, i in lst:
label = self.output_labels[i]
b1 = (label[0] == 'left_arc'
or label[0] == 'right_arc') and not state.stack
b2 = label[0] == 'shift' and len(
state.buffer) == 1 and state.stack
b3 = state.stack and state.stack[-1] == 0 and label[
0] == 'left_arc'
if b1 or b2 or b3:
continue
break
if label[0] == "shift":
state.shift()
elif label[0] == "left_arc":
state.left_arc(label[1])
elif label[0] == "right_arc":
state.right_arc(label[1])
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
pass
# TODO: Write the body of this loop for part 4
single_vector = self.extractor.get_input_representation(
words, pos, state)
possible_actions = list(self.model.predict(single_vector)[0])
#Reference taken from https://docs.python.org/3/howto/sorting.html
sorted_actions = [
i[0] for i in sorted(enumerate(possible_actions),
reverse=True,
key=lambda other: other[1])
]
j = 0
transition = self.output_labels[sorted_actions[j]][0]
while ((len(state.stack) == 0
and transition in {"right_arc", "left_arc"})
or (len(state.stack) > 0 and len(state.buffer) == 1
and transition == "shift")
or (len(state.stack) > 0 and state.stack[-1] == 0
and transition == "left_arc")):
j += 1
transition = self.output_labels[sorted_actions[j]][0]
if self.output_labels[sorted_actions[j]][1] == None:
state.shift()
else:
if self.output_labels[sorted_actions[j]][0] == "left_arc":
state.left_arc(self.output_labels[sorted_actions[j]][1])
elif self.output_labels[sorted_actions[j]][0] == "right_arc":
state.right_arc(self.output_labels[sorted_actions[j]][1])
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
feature = self.extractor.get_input_representation(
words, pos, state)
np.reshape(feature, (6, 1))
pre = self.model.predict(
np.array([
self.extractor.get_input_representation(words, pos, state),
]))
list1 = np.flipud(np.argsort(pre)[0])
for i in list1:
act = self.output_labels[i]
rel, label = act
if rel == "shift":
if len(state.buffer) == 1 and len(state.stack) > 0:
continue
else:
state.shift()
break
elif rel == "left_arc":
if len(state.stack) == 0 or state.stack[-1] == 0:
continue
else:
state.left_arc(label)
break
elif rel == "right_arc":
if len(state.stack) == 0:
continue
else:
state.right_arc(label)
break
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
# print(state)
while state.buffer:
features = self.extractor.get_input_representation(
words, pos, state)
possible_actions = self.model.predict(np.vstack([features]))
probs = [(idx, prob)
for idx, prob in enumerate(possible_actions[0])]
# Sort it according to the output probabilities
sorted_probs = sorted(probs, key=lambda x: x[1], reverse=True)
# print(sorted_probs)
for sp in sorted_probs:
output_label = self.output_labels[sp[0]]
transition, label = output_label
if transition == 'shift':
# Check illegal case 1: Shifting the only word out of the buffer is illegal, unless the stack is empty
if len(state.buffer) == 1 and state.stack != []:
continue
state.shift()
break
# Check illegal case 2: arc-left is not permitted if the stack is empty
# Check illegal case 3: the root node must never be the target of a left-arc
elif transition == 'left_arc' and state.stack != [] and state.stack[
-1] != 0:
state.left_arc(label)
break
# Check illegal case 2: arc-right is not permitted if the stack is empty
elif transition == 'right_arc' and state.stack != []:
state.right_arc(label)
break
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
feature = self.extractor.get_input_representation(
words, pos, state)
pred = self.model.predict(np.array([
feature,
]))
indices = np.argsort(-pred, axis=1)[0]
for i in indices:
key, val = self.output_labels[i]
if key == 'shift':
if len(state.buffer) == 1:
if len(state.stack) == 0:
state.shift()
break
elif len(state.buffer) > 1:
state.shift()
break
elif key == 'left_arc':
if len(state.stack) > 0:
if val != 'root':
state.left_arc(val)
break
elif key == 'right_arc':
if len(state.stack) != 0:
state.right_arc(val)
break
# TODO: Write the body of this loop for part 4
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
data = self.extractor.get_input_representation(words, pos, state)
data = data.reshape(1, 6)
vector = self.model.predict(data)
vector = vector[0]
if len(state.stack) == 0:
state.shift()
continue
elif len(state.buffer) <= 1:
vector[0] = 0.0
arc_index = 0
for i in range(0, len(vector)):
if state.stack[-1] == 0 and self.output_labels[i][
0] == "left_arc":
vector[i] = 0.0
continue
if vector[i] > vector[arc_index]:
arc_index = i
pair = self.output_labels[arc_index]
if pair[0] == "left_arc":
state.left_arc(pair[1])
elif pair[0] == "shift":
state.shift()
else:
state.right_arc(pair[1])
# TODO: Write the body of this loop for part 4
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
#print(words)
#print(pos)
while state.buffer:
# TODO: Write the body of this loop for part 4
# print("The Buffer is", state.buffer)
# print("The Stack is", state.stack)
inp_vector = self.extractor.get_input_representation(
words, pos, state)
# final_vector = np.zeros(1)
# final_vector[0] = inp_vector
inp_vector_trans = inp_vector.transpose()
inp_vector_trans = inp_vector_trans.reshape(1, -1)
#print(inp_vector_trans.shape)
preds = self.model.predict(inp_vector_trans)
# preds[::-1].sort()
# print(preds)
predsList = list(preds[0])
#print(predsList)
predsList = np.argsort(predsList)
# print(predsList)
i = len(predsList) - 1
# print(i)
# print(self.output_labels)
while i >= 0:
action = self.output_labels[predsList[i]]
# print(action)
if len(state.stack) == 0 and (action[0] == "left_arc"
or action[0] == "right_arc"):
i = i - 1
elif len(state.buffer) == 1 and len(
state.stack) != 0 and action[0] == "shift":
i = i - 1
elif len(state.stack) != 0 and state.stack[-1] == 0 and action[
0] == "left_arc":
i = i - 1
else:
# print("Reaches Else")
if action[0] == "left_arc":
state.left_arc(action[1])
elif action[0] == "right_arc":
state.right_arc(action[1])
else:
state.shift()
break
# print("Reaches end of inner")
# print("Reaches Here")
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1, len(words)))
state.stack.append(0)
while state.buffer:
pass
# TODO: Write the body of this loop for part 4
dep_relations = [
'tmod', 'vmod', 'csubjpass', 'rcmod', 'ccomp', 'poss',
'parataxis', 'appos', 'dep', 'iobj', 'pobj', 'mwe', 'quantmod',
'acomp', 'number', 'csubj', 'root', 'auxpass', 'prep', 'mark',
'expl', 'cc', 'npadvmod', 'prt', 'nsubj', 'advmod', 'conj',
'advcl', 'punct', 'aux', 'pcomp', 'discourse', 'nsubjpass',
'predet', 'cop', 'possessive', 'nn', 'xcomp', 'preconj', 'num',
'amod', 'dobj', 'neg', 'dt', 'det'
]
output_labels_2 = {}
output_labels_2[0] = ('shift', None)
count3 = 1
for dep in dep_relations:
output_labels_2[count3] = ('left_arc', dep)
count3 = count3 + 1
count4 = 46
for dep in dep_relations:
output_labels_2[count4] = ('right_arc', dep)
count4 = count4 + 1
# citation: https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html
in_rep = np.reshape(
self.extractor.get_input_representation(words, pos, state),
(-1, 6))
probs = self.model.predict(in_rep)
# citation: https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flatten.html
probs = probs.flatten()
count = 0
probs_new = []
for prob in probs:
probs_new.append((prob, count))
count = count + 1
probs_new.sort()
count2 = -1
proceed = True
while proceed:
highest_prob = probs_new[count2][1]
if len(state.stack) == 0 and (
output_labels_2[highest_prob][0] == 'left_arc'
or output_labels_2[highest_prob][0] == 'right_arc'):
count2 = count2 - 1
elif len(
state.stack
) == 1 and output_labels_2[highest_prob][0] == 'left_arc':
count2 = count2 - 1
elif len(state.stack) > 0 and len(
state.buffer
) == 1 and output_labels_2[highest_prob][0] == 'shift':
count2 = count2 - 1
elif output_labels_2[highest_prob][
0] == 'left_arc' and highest_prob > 45:
count2 = count2 - 1
elif output_labels_2[highest_prob][
0] == 'right_arc' and highest_prob < 46:
count2 = count2 - 1
elif output_labels_2[highest_prob][
0] == 'shift' and highest_prob > 0:
count2 = count2 - 1
else:
if output_labels_2[highest_prob][0] == 'left_arc':
state.left_arc(output_labels_2[highest_prob][1])
proceed = False
elif output_labels_2[highest_prob][0] == 'right_arc':
state.right_arc(output_labels_2[highest_prob][1])
proceed = False
elif output_labels_2[highest_prob][0] == 'shift':
state.shift()
proceed = False
result = DependencyStructure()
for p, c, r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c], p, r))
return result
def parse_sentence(self, words, pos):
state = State(range(1,len(words)))
state.stack.append(0)
while state.buffer:
arr = np.reshape(self.extractor.get_input_representation(words,pos,state),(-1,6))
trans = self.model.predict(arr)
trans = trans.flatten()
trans_sorted = np.sort(trans)
k = 1
max_prob = trans_sorted[-k]
ind = np.where(trans == max_prob)
transition = self.output_labels[ind[0][0]]
boo = 'Not done'
while boo == 'Not done':
if len(state.stack) > 0 and transition[0] == 'left_arc':
state.left_arc(transition[1])
boo = 'Done'
elif len(state.stack) > 0 and transition[0] == 'right_arc':
state.right_arc(transition[1])
boo = 'Done'
elif len(state.stack) == 0 and transition[0] == 'shift' and len(state.buffer) > 0:
state.shift()
boo = 'Done'
elif len(state.stack) > 0 and transition[0] == 'shift' and len(state.buffer) > 1:
state.shift()
boo = 'Done'
elif state.stack[-1] == 0 and transition[0] != 'left_arc':
state.left_arc(transition[1])
boo = 'Done'
else:
k += 1
max_prob = trans_sorted[-k]
ind = np.where(trans == max_prob)
transition = self.output_labels[ind[0][0]]
result = DependencyStructure()
for p,c,r in state.deps:
result.add_deprel(DependencyEdge(c,words[c],pos[c],p, r))
return result
continue
else:
print("03")
state.right_arc(dep_relations[i-45])
break
else:
if state.stack == []:
print(1)
continue
elif len(state.stack) == 1:
print(1)
continue
else:
print("04")
state.left_arc(dep_relations[i])
break
'''
result = DependencyStructure()
for p,c,r in state.deps:
result.add_deprel(DependencyEdge(c, words[c], pos[c],p, r))
print(result)
'''