def extract(stack, queue, state, feature_names, sentence):
"""
Extract the features from one sentence
returns X and y, where X is a list of dictionaries and
y is a list of symbols
:param sentence:
:param w_size:
:return:
"""
x = []
tmpListStack = ['nil', 'nil', 'nil', 'nil']
tmpListQueue = ['nil', 'nil', 'nil', 'nil']
try:
tmpListStack[0] = stack[0]['form']
tmpListStack[1] = stack[0]['postag']
tmpListStack[2] = stack[1]['form']
tmpListStack[3] = stack[1]['postag']
except:
pass
try:
tmpListQueue[0] = queue[0]['form']
tmpListQueue[1] = queue[0]['postag']
tmpListQueue[2] = queue[1]['form']
tmpListQueue[3] = queue[1]['postag']
except:
pass
x.extend(tmpListStack)
x.extend(tmpListQueue)
x.append(transition.can_rightarc(stack))
x.append(transition.can_reduce(stack, state))
# We represent the feature vector as a dictionary
# The classes are stored in a list
#y.append(padded_sentence[i + w_size][2])
return dict(zip(feature_names, x))
def reference(stack, queue, graph):
"""
Gold standard parsing
Produces a sequence of transitions from a manually-annotated corpus:
sh, re, ra.deprel, la.deprel
:param stack: The stack
:param queue: The input list
:param graph: The set of relations already parsed
:return: the transition and the grammatical function (deprel) in the
form of transition.deprel
"""
# Right arc
if stack and stack[0]['id'] == queue[0]['head']:
# print('ra', queue[0]['deprel'], stack[0]['cpostag'], queue[0]['cpostag'])
deprel = '.' + queue[0]['deprel']
stack, queue, graph = transition.right_arc(stack, queue, graph)
return stack, queue, graph, 'ra' + deprel
# Left arc
if stack and queue[0]['id'] == stack[0]['head']:
# print('la', stack[0]['deprel'], stack[0]['cpostag'], queue[0]['cpostag'])
deprel = '.' + stack[0]['deprel']
stack, queue, graph = transition.left_arc(stack, queue, graph)
return stack, queue, graph, 'la' + deprel
# Reduce
if stack and transition.can_reduce(stack, graph):
for word in stack:
if (word['id'] == queue[0]['head'] or
word['head'] == queue[0]['id']):
# print('re', stack[0]['cpostag'], queue[0]['cpostag'])
stack, queue, graph = transition.reduce(stack, queue, graph)
return stack, queue, graph, 're'
# Shift
# print('sh', [], queue[0]['cpostag'])
stack, queue, graph = transition.shift(stack, queue, graph)
return stack, queue, graph, 'sh'
def line_extract(stack, queue, graph, feature_names, sentence, samples,
special):
x = []
structures = [stack, queue]
elements = ['postag', 'form']
for structure in structures:
for element in elements:
for i in range(samples):
if len(structure) > i:
x.append(structure[i][element])
else:
x.append('nil')
if special:
# word before and after top of stack
for element in elements:
for i in [-1, 1]:
if len(stack) > 0:
index = int(stack[0]['id']) + i
if 0 <= index < len(sentence):
x.append(sentence[index][element])
else:
x.append('nil')
else:
x.append('nil')
x.append(transition.can_reduce(stack, graph))
x.append(transition.can_leftarc(stack, graph))
return dict(zip(feature_names, x))
def reference(stack, queue, graph):
"""
Gold standard parsing
Produces a sequence of transitions from a manually-annotated corpus:
sh, re, ra.deprel, la.deprel
:param stack: The stack
:param queue: The input list
:param graph: The set of relations already parsed
:return: the transition and the grammatical function (deprel) in the
form of transition.deprel
"""
# Right arc
if stack and stack[0]['id'] == queue[0]['head']:
# print('ra', queue[0]['deprel'], stack[0]['cpostag'], queue[0]['cpostag'])
deprel = '.' + queue[0]['deprel']
stack, queue, graph = transition.right_arc(stack, queue, graph)
return stack, queue, graph, 'ra' + deprel
# Left arc
if stack and queue[0]['id'] == stack[0]['head']:
# print('la', stack[0]['deprel'], stack[0]['cpostag'], queue[0]['cpostag'])
deprel = '.' + stack[0]['deprel']
stack, queue, graph = transition.left_arc(stack, queue, graph)
return stack, queue, graph, 'la' + deprel
# Reduce
if stack and transition.can_reduce(stack, graph):
for word in stack:
if (word['id'] == queue[0]['head'] or
word['head'] == queue[0]['id']):
# print('re', stack[0]['cpostag'], queue[0]['cpostag'])
stack, queue, graph = transition.reduce(stack, queue, graph)
return stack, queue, graph, 're'
# Shift
# print('sh', [], queue[0]['cpostag'])
stack, queue, graph = transition.shift(stack, queue, graph)
return stack, queue, graph, 'sh'
def extract2(stack, queue, graph, feature_names, sentence):
features = {}
feat_vec = ['', '', '', '', '', '', '', '', '', '']
if not stack:
feat_vec[0] = feat_vec[1] = feat_vec[2] = feat_vec[3] = 'nil'
elif len(stack) < 2:
feat_vec[1] = feat_vec[3] = 'nil'
feat_vec[0] = stack[0]['postag']
feat_vec[2] = stack[0]['form']
else:
feat_vec[0] = stack[0]['postag']
feat_vec[1] = stack[1]['postag']
feat_vec[2] = stack[0]['form']
feat_vec[3] = stack[1]['form']
if not queue:
feat_vec[5] = feat_vec[6] = feat_vec[7] = feat_vec[8] = 'nil'
if len(queue) < 2:
feat_vec[4] = queue[0]['postag']
feat_vec[6] = queue[0]['form']
feat_vec[5] = feat_vec[7] = 'nil'
else:
feat_vec[4] = queue[0]['postag']
feat_vec[5] = queue[1]['postag']
feat_vec[6] = queue[0]['form']
feat_vec[7] = queue[1]['form']
feat_vec[8] = transition.can_reduce(stack, graph)
feat_vec[9] = transition.can_leftarc(stack, graph)
return dict(zip(feature_names, feat_vec))
def extract_2(stack, queue, graph, feature_names, sentence):
stack_list = ["nil", "nil", "nil", "nil"]
if stack:
stack_list[0] = stack[0]['form']
stack_list[1] = stack[0]['postag']
if len(stack) > 1:
stack_list[2] = stack[1]['form']
stack_list[3] = stack[1]['postag']
queue_list = ["nil", "nil", "nil", "nil"]
if queue:
queue_list[0] = queue[0]['form']
queue_list[1] = queue[0]['postag']
if len(queue) > 1:
queue_list[2] = queue[1]['form']
queue_list[3] = queue[1]['postag']
features = stack_list + queue_list
can_re = transition.can_reduce(stack, graph)
can_left_arc = transition.can_leftarc(stack, graph)
features.append(can_re)
features.append(can_left_arc)
features = zip(feature_names, features)
features = dict(features)
# print(features)
return features
def extract(stack, queue, graph, feature_names, sentence):
# X contains one dict for each word, with each feature as a key in the dict
# x is a row in X
x = list()
p_stack = stack + [{'id': '-1', 'form': 'nil', 'postag': 'nil'}]*2
p_queue = queue + [{'form': 'nil', 'postag': 'nil'}]*2
p_sentence = [{'form': 'nil', 'postag': 'nil'}, {'form': 'BOS', 'postag': 'BOS'}] + sentence[1:] + [{'form': 'EOS', 'postag': 'EOS'}]
# 1st feature set
x.append(transition.can_reduce(stack, graph))
x.append(transition.can_leftarc(stack, graph))
x.append(p_stack[0]['postag'])
x.append(p_stack[0]['form'])
x.append(p_queue[0]['postag'])
x.append(p_queue[0]['form'])
# 2nd feature set
x.append(p_stack[1]['postag'])
x.append(p_stack[1]['form'])
x.append(p_queue[1]['postag'])
x.append(p_queue[1]['form'])
# 3rd feature set
i = int(p_stack[0]['id'])
if i == -1:
x += ['nil']*4
else:
w_pre, w_next = p_sentence[i], p_sentence[i + 2]
x.append(w_pre['postag'])
x.append(w_pre['form'])
x.append(w_next['postag'])
x.append(w_next['form'])
return dict(zip(feature_names, x))
def extract(stack, queue, graph, feature_names, sentence):
full_sentence = sentence
features = []
try:
features.append(stack[0]['postag'])
features.append(stack[0]['form'])
except:
features.append("nil")
features.append("nil")
features.append(queue[0]['postag'])
features.append(queue[0]['form'])
if transition.can_leftarc(stack, graph):
can_la = True
else:
can_la = False
if transition.can_reduce(stack, graph):
can_re = True
else:
can_re = False
features.append(can_re)
features.append(can_la)
return features
def generate_feature_vector2(stack,queue,graph):
feature_names = ['stack0_POS','stack1_POS','stack0_word','stack1_word',
'queue0_POS','queue1_POS','queue0_word','queue1_word','can-re','can-la']
try:
stack0_POS = stack[0]['postag']
except IndexError:
stack0_POS = 'nil'
try:
stack1_POS = stack[1]['postag']
except IndexError:
stack1_POS = 'nil'
try:
stack0_word = stack[0]['form']
except IndexError:
stack0_word = 'nil'
try:
stack1_word = stack[1]['form']
except IndexError:
stack1_word = 'nil'
#Guaranteed to exist because of while loop
queue0_POS = queue[0]['postag']
queue0_word= queue[0]['form']
try:
queue1_POS = queue[1]['postag']
except IndexError:
queue1_POS = 'nil'
try:
queue1_word= queue[1]['form']
except IndexError:
queue1_word = 'nil'
can_left_arc = transition.can_leftarc(stack,graph)
can_reduce = transition.can_reduce(stack,graph)
return dict(zip(feature_names, [stack0_POS,stack1_POS,stack0_word,stack1_word,queue0_POS,queue1_POS,queue0_word,queue1_word,can_reduce,can_left_arc]))
def extract_features_sent(sentence, feature_names):
"""
Extract the features from one sentence
returns X and y, where X is a list of dictionaries and
y is a list of symbols
:param sentence:
:param w_size:
:return:
"""
#sentence = sentence.splitlines()
stack = []
graph = {}
queue = list(sentence)
graph['heads'] = {}
graph['heads']['0'] = '0'
graph['deprels'] = {}
graph['deprels']['0'] = 'ROOT'
transitions = []
x = list()
X = list()
y = list()
while queue:
if (len(stack) > 0):
x.append(stack[0]['cpostag'])
x.append(stack[0]['form'])
else:
x.append('nil')
x.append('nil')
if (queue):
x.append(queue[0]['cpostag'])
x.append(queue[0]['form'])
else:
x.append('nil')
x.append('nil')
x.append(transition.can_reduce(stack, graph))
x.append(transition.can_leftarc(stack, graph))
X.append(dict(zip(feature_names, x)))
#remove reference, predict what action should be done(equiv to trans)
stack, queue, graph, trans = dparser.reference(stack, queue, graph)
y.append(trans)
x = list()
#stack, graph = transition.empty_stack(stack, graph)
#for word in queue:
#print(word['form'])
#stack, queue, graph, trans = reference(stack, queue, graph)
#transitions.append(trans)
# stack, graph = transition.empty_stack(stack, graph)
return X, y
def parse_ml(stack, queue, graph, trans):
if stack and trans[:2] == 'ra' and transition.can_rightarc(stack):
stack, queue, graph = transition.right_arc(stack, queue, graph, trans[3:])
return stack, queue, graph, 'ra'
if stack and trans[:2] == 'la' and transition.can_leftarc(stack, graph):
stack, queue, graph = transition.left_arc(stack, queue, graph, trans[3:])
return stack, queue, graph, 'la'
if stack and trans[:2] == 're' and transition.can_reduce(stack, graph):
stack, queue, graph = transition.reduce(stack, queue, graph)
return stack, queue, graph, 're'
stack, queue, graph = transition.shift(stack, queue, graph)
return stack, queue, graph, 'sh'
def reference(stack, queue, graph):
"""
Gold standard parsing
Produces a sequence of transitions from a manually-annotated corpus:
sh, re, ra.deprel, la.deprel
:param stack: The stack
:param queue: The input list
:param graph: The set of relations already parsed
:return: the transition and the grammatical function (deprel) in the
form of transition.deprel
"""
# This is a continuous list of if statements, but each has a return statement inside of it, so it behaves similar to
# a set of elif statements. However, there is a priority level to these operations,
# 1) Right-arc, 2) Left-arc, 3) Reduce, 4) Shift
# Right arc
if stack and stack[0]['id'] == queue[0]['head']:
# If stack is non-null and
# the top of the stack's id key is the same as the front of the queue's head key are the same
# Then we do a right-arc
# print('ra', queue[0]['deprel'], stack[0]['cpostag'], queue[0]['cpostag'])
deprel = '.' + queue[0]['deprel']
stack, queue, graph = transition.right_arc(stack, queue, graph)
return stack, queue, graph, 'ra' + deprel
# Left arc
if stack and queue[0]['id'] == stack[0]['head']:
# If the stack is non-null AND
# the front of the queue's ID is the same as the top of the stack's head value
# Then we do a left-arc
# print('la', stack[0]['deprel'], stack[0]['cpostag'], queue[0]['cpostag'])
deprel = '.' + stack[0]['deprel']
stack, queue, graph = transition.left_arc(stack, queue, graph)
return stack, queue, graph, 'la' + deprel
# Reduce
if stack and transition.can_reduce(stack, graph):
# If the stack is non-null and the stack can be reduced, then
for word in stack:
# For each word present in the stack
if (word['id'] == queue[0]['head'] or
# If the word's ID is the same as the front of the queue's HEAD value
word['head'] == queue[0]['id']):
# OR the word's HEAD value matches the front of the queue's ID value
# print('re', stack[0]['cpostag'], queue[0]['cpostag'])
# Then we can reduce the value that is on the stack
stack, queue, graph = transition.reduce(stack, queue, graph)
return stack, queue, graph, 're'
# Shift
# We will only get here if none of the other operations did NOT occur
# This is because each of the if statements for the operations has a return statement
# Stack was pushed to, queue had front removed, and graph is unmodified by shift actions
stack, queue, graph = transition.shift(stack, queue, graph)
return stack, queue, graph, 'sh'
def extract3(stack, queue, graph, feature_names, sentence):
features = {}
feat_vec = ['', '', '', '', '', '', '', '', '', '', '', '', '', '']
if not stack:
feat_vec[0] = feat_vec[1] = feat_vec[2] = feat_vec[3] = 'nil'
elif len(stack) < 2:
feat_vec[1] = feat_vec[3] = 'nil'
feat_vec[0] = stack[0]['postag']
feat_vec[2] = stack[0]['form']
else:
feat_vec[0] = stack[0]['postag']
feat_vec[1] = stack[1]['postag']
feat_vec[2] = stack[0]['form']
feat_vec[3] = stack[1]['form']
if not queue:
feat_vec[5] = feat_vec[6] = feat_vec[7] = feat_vec[8] = 'nil'
if len(queue) < 2:
feat_vec[4] = queue[0]['postag']
feat_vec[6] = queue[0]['form']
feat_vec[5] = feat_vec[7] = 'nil'
else:
feat_vec[4] = queue[0]['postag']
feat_vec[5] = queue[1]['postag']
feat_vec[6] = queue[0]['form']
feat_vec[7] = queue[1]['form']
feat_vec[8] = transition.can_reduce(stack, graph)
feat_vec[9] = transition.can_leftarc(stack, graph)
#before 10, 11
#after 12,13
if not stack:
feat_vec[10] = feat_vec[11] = feat_vec[12] = feat_vec[13] = 'nil'
else:
st_id = stack[0]['id']
if int(st_id) == 0:
feat_vec[10] = feat_vec[11] = 'nil'
else:
feat_vec[10] = sentence[int(st_id) - 1]['postag']
feat_vec[11] = sentence[int(st_id) - 1]['form']
if int(st_id) > len(sentence):
feat_vec[12] = feat_vec[13] = 'nil'
else:
feat_vec[12] = sentence[int(st_id) + 1]['postag']
feat_vec[13] = sentence[int(st_id) + 1]['form']
return dict(zip(feature_names, feat_vec))
def extract(stack, queue, graph, feature_names, sentence):
X = []
X.append(transition.can_leftarc(stack, graph))
X.append(transition.can_reduce(stack, graph))
try:
X.append(stack[0]['postag'])
X.append(stack[0]['form'])
except:
X.append("nil")
X.append("nil")
try:
X.append(stack[1]['postag'])
X.append(stack[1]['form'])
except:
X.append("nil")
X.append("nil")
X1 = X
try:
X.append(queue[0]['postag'])
X.append(queue[0]['form'])
except:
X.append("nil")
X.append("nil")
try:
X.append(queue[1]['postag'])
X.append(queue[1]['form'])
except:
X.append("nil")
X.append("nil")
X2 = X
try:
for i in range(len(sentence)):
if sentence[i]['form'] == stack[0]['form']:
X.append(sentence[i + 1]['postag'])
X.append(sentence[i + 1]['form'])
except:
X.append("nil")
X.append("nil")
try:
X.append(sentence[int(stack[1]['head'])]['postag'])
X.append(sentence[int(stack[1]['head'])]['form'])
except:
X.append("nil")
X.append("nil")
X3 = X
X1 = dict(zip(feature_names[:6], X1))
X2 = dict(zip(feature_names[:10], X2))
X3 = dict(zip(feature_names, X3))
return X1, X2, X3
def extract_features_sent(sentence, feature_names, classifier, dict_classes,
vec):
stack = []
graph = {}
queue = list(sentence)
graph['heads'] = {}
graph['heads']['0'] = '0'
graph['deprels'] = {}
graph['deprels']['0'] = 'ROOT'
transitions = []
x = list()
X = list()
y = list()
while queue:
if (len(stack) > 0):
x.append(stack[0]['cpostag'])
x.append(stack[0]['form'])
else:
x.append('nil')
x.append('nil')
if (queue):
x.append(queue[0]['cpostag'])
x.append(queue[0]['form'])
else:
x.append('nil')
x.append('nil')
x.append(transition.can_reduce(stack, graph))
x.append(transition.can_leftarc(stack, graph))
X = (dict(zip(feature_names, x)))
#remove reference, predict what action should be done(equiv to trans)
#print('Stack is ', len(stack))
#print('Queue is ', queue)
trans_nr = classifier.predict(vec.transform(X))
print(trans_nr[0])
trans = dict_classes[trans_nr[0]]
stack, queue, graph, trans = parse_ml(stack, queue, graph, trans)
x = list()
#stack, graph = transition.empty_stack(stack, graph)
transition.empty_stack(stack, graph)
for word in sentence:
word['head'] = graph['heads'][word['id']]
word['deprel'] = graph['deprels'][word['id']]
return graph
def extract_mode_1(stack, queue, graph, feature_names, sentence=None):
features = list()
if stack:
stack0 = stack[0]
features.extend([stack0.get('postag'), stack0.get('form')])
else:
features.extend(['nil', 'nil'])
if queue:
queue0 = queue[0]
features.extend([queue0.get('postag'), queue0.get('form')])
else:
features.extend(['nil', 'nil'])
features.append(transition.can_reduce(stack, graph))
features.append(transition.can_leftarc(stack, graph))
return dict(zip(feature_names.get('mode1'), features))
def extract(stack, queue, graph, feature_names, sentence):
features = []
features.append(stack[0]['postag'] if len(stack) > 0 else 'nil')
features.append(stack[1]['postag'] if len(stack) > 1 else 'nil')
features.append(stack[0]['form'] if len(stack) > 0 else 'nil')
features.append(stack[1]['form'] if len(stack) > 1 else 'nil')
features.append(queue[0]['postag'] if len(queue) > 0 else 'nil')
features.append(queue[1]['postag'] if len(queue) > 1 else 'nil')
features.append(queue[0]['form'] if len(queue) > 0 else 'nil')
features.append(queue[1]['form'] if len(queue) > 1 else 'nil')
features.append(transition.can_reduce(stack, graph))
features.append(transition.can_leftarc(stack, graph))
return dict(zip(feature_names, features))
def extract(stack, queue, state, feature_names, sentence):
features = {}
features["can_reduce"] = str(transition.can_reduce(stack, state))
features["can_leftarc"] = str(transition.can_leftarc(stack, state))
if stack:
features["stack0_postag"] = stack[0]["postag"]
features["stack0_form"] = stack[0]["form"]
else:
features["stack0_postag"] = "nil"
features["stack0_form"] = "nil"
if len(stack) > 1:
features["stack1_postag"] = stack[1]["postag"]
features["stack1_form"] = stack[1]["form"]
else:
features["stack1_postag"] = "nil"
features["stack1_form"] = "nil"
if queue:
features["queue0_postag"] = queue[0]["postag"]
features["queue0_form"] = queue[0]["form"]
else:
features["queue0_postag"] = "nil"
features["queue0_form"] = "nil"
if len(queue) > 1:
features["queue1_postag"] = queue[1]["postag"]
features["queue1_form"] = queue[1]["form"]
else:
features["queue1_postag"] = "nil"
features["queue1_form"] = "nil"
features["nextWord_form"] = "nil"
features["nextWord_postag"] = "nil"
if int(queue[0]["id"]) < len(sentence) - 1:
w = sentence[int(queue[0]["id"]) + 1]
features["nextWord_form"] = w['form']
features["nextWord_postag"] = w['postag']
features["prevWord_form"] = "nil"
features["prevWord_postag"] = "nil"
if int(queue[0]["id"]) > 0:
w = sentence[int(queue[0]["id"]) - 1]
features["prevWord_form"] = w['form']
features["prevWord_postag"] = w['postag']
return features
def parse_ml(stack, queue, graph, trans):
#print(trans)
if stack and transition.can_rightarc(stack) and trans[:2] == 'ra':
stack, queue, graph = transition.right_arc(stack, queue, graph,
trans[3:])
return stack, queue, graph, 'ra'
elif stack and transition.can_leftarc(
stack, graph) and trans[:2] == 'la': #VARFÖR :2
stack, queue, graph = transition.left_arc(stack, queue, graph,
trans[3:])
return stack, queue, graph, 'la'
elif stack and transition.can_reduce(stack, graph) and trans[:2] == 're':
stack, queue, graph = transition.reduce(stack, queue, graph)
return stack, queue, graph, 're'
else:
stack, queue, graph = transition.shift(stack, queue, graph)
return stack, queue, graph, "sh"
def extract(stack, queue, graph, feature_names, sentence):
features = []
# Feature set 1
features.extend(['nil', 'nil', 'nil', 'nil', 'nil', 'nil'])
if len(stack) >= 1:
features[0] = stack[0]['form']
features[1] = stack[0]['postag']
if len(queue) >= 1:
features[2] = queue[0]['form']
features[3] = queue[0]['postag']
features[4] = transition.can_leftarc(stack, graph)
features[5] = transition.can_reduce(stack, graph)
# Feature set 2
if len(feature_names) == 10 or len(feature_names) == 14:
features.extend(['nil', 'nil', 'nil', 'nil'])
if len(stack) >= 2:
features[6] = stack[1]['form']
features[7] = stack[1]['postag']
if len(queue) >= 2:
features[8] = queue[1]['form']
features[9] = queue[1]['postag']
# Feature set 3
if len(feature_names) == 14:
features.extend(['nil', 'nil', 'nil', 'nil'])
if len(stack) >= 1 and len(sentence) > int(stack[0]['id']) + 1:
word = sentence[int(stack[0]['id']) + 1]
features[10] = word['form']
features[11] = word['postag']
if len(queue) >= 1 and len(sentence) > int(queue[0]['id']) + 1:
word = sentence[int(queue[0]['id']) + 1]
features[12] = word['form']
features[13] = word['postag']
features = dict(zip(feature_names, features))
return features
def parse_ml(stack, queue, graph, trans):
#right arc
if stack and trans[:2] == 'ra' and transition.can_rightarc(stack):
stack, queue, graph = transition.right_arc(stack, queue, graph,
trans[3:])
return stack, queue, graph, 'ra'
#left arc
if stack and trans[:2] == 'la' and transition.can_leftarc(stack, graph):
stack, queue, graph = transition.left_arc(stack, queue, graph,
trans[3:])
return stack, queue, graph, 'la'
#reduce
if stack and trans[:2] == 're' and transition.can_reduce(stack, graph):
stack, queue, graph = transition.reduce(stack, queue, graph)
return stack, queue, graph, 're'
#shift
if stack and trans[:2] == 'sh':
stack, queue, graph = transition.shift(stack, queue, graph)
return stack, queue, graph, 'sh'
#action not possible -> shift
else:
stack, queue, graph = transition.shift(stack, queue, graph)
return stack, queue, graph, 'sh'
def generate_feature_vector3(stack,queue,graph,sentence):
feature_names = ['stack0_POS','stack1_POS','stack0_word','stack1_word',
'queue0_POS','queue1_POS','queue0_word','queue1_word','can-re','can-la','following_word','following_word_POS','queue3_POS','stack0_previous_word_POS']
try:
stack0_POS = stack[0]['postag']
except IndexError:
stack0_POS = 'nil'
try:
stack1_POS = stack[1]['postag']
except IndexError:
stack1_POS = 'nil'
try:
stack0_word = stack[0]['form']
except IndexError:
stack0_word = 'nil'
try:
stack1_word = stack[1]['form']
except IndexError:
stack1_word = 'nil'
#Guaranteed to exist because of while loop
queue0_POS = queue[0]['postag']
queue0_word= queue[0]['form']
try:
queue1_POS = queue[1]['postag']
except IndexError:
queue1_POS = 'nil'
try:
queue1_word= queue[1]['form']
except IndexError:
queue1_word = 'nil'
#POS QUEUE 3
try:
queue3_POS = queue[3]['postag']
except IndexError:
queue3_POS = 'nil'
#POS STACK 0 pw
try:
idx = stack[0]['id']
stack0_previous_word_POS= sentence[int(idx)-1]['postag']
except IndexError:
stack0_previous_word_POS= 'nil'
#POS STACK 0 fw
try:
idx = stack[0]['id']
following_word_POS = sentence[int(idx)+1]['postag']
except IndexError:
following_word_POS = 'nil'
#LEX STACK 0 fw
try:
idx = stack[0]['id']
following_word = sentence[int(idx)+1]['form']
except IndexError:
following_word = 'nil'
can_left_arc = transition.can_leftarc(stack,graph)
can_reduce = transition.can_reduce(stack,graph)
return dict(zip(feature_names, [stack0_POS,stack1_POS,stack0_word,stack1_word,queue0_POS,queue1_POS,
queue0_word,queue1_word,can_reduce,can_left_arc,following_word,following_word_POS,queue3_POS,stack0_previous_word_POS]))
def extract(stack, queue, graph, feature_names, sentence):
features = {}
for fn in feature_names:
if fn == 'stack0_POS':
if stack:
features["stack0_POS"] = stack[0]["postag"]
else:
features["stack0_POS"] = "nil"
if fn == 'stack1_POS':
if len(stack) > 1:
features["stack1_POS"] = stack[1]["postag"]
else:
features["stack1_POS"] = "nil"
if fn == 'stack0_word':
if stack:
features["stack0_word"] = stack[0]["form"]
else:
features["stack0_word"] = "nil"
if fn == 'stack1_word':
if stack and len(stack) > 1:
features["stack1_word"] = stack[1]["form"]
else:
features["stack1_word"] = "nil"
if fn == 'queue0_POS':
if queue:
features["queue0_POS"] = queue[0]["postag"]
else:
features["queue0_POS"] = "nil"
if fn == 'queue1_POS':
if queue and len(queue) > 1:
features["queue1_POS"] = queue[1]["postag"]
else:
features["queue1_POS"] = "nil"
if fn == 'queue0_word':
if queue:
features["queue0_word"] = queue[0]["form"]
else:
features["queue0_word"] = "nil"
if fn == 'queue1_word':
if queue and len(queue) > 1:
features["queue1_word"] = queue[1]["form"]
else:
features["queue1_word"] = "nil"
if fn == 'can-re':
features["can-re"] = str(transition.can_reduce(stack, graph))
if fn == 'can-la':
features["can-la"] = str(transition.can_leftarc(stack, graph))
if fn == 'next_word_POS':
features["next_word_POS"] = "nil"
if int(queue[0]["id"]) < len(sentence) - 1:
#Next sentece +1
w = sentence[int(queue[0]["id"]) + 1]
features["next_word_POS"] = w['postag']
if fn == 'next_word':
features["next_word"] = "nil"
if int(queue[0]["id"]) < len(sentence) - 1:
#Next sentece +1
w = sentence[int(queue[0]["id"]) + 1]
features["next_word"] = w['form']
if fn == 'prev_word_POS':
features["prev_word_POS"] = "nil"
if int(queue[0]["id"]) < len(sentence) - 1:
# prev sentece -1
w = sentence[int(queue[0]["id"]) - 1]
features["prev_word_POS"] = w['postag']
if fn == 'prev_word':
features["prev_word"] = "nil"
if int(queue[0]["id"]) < len(sentence) - 1:
# prev sentece -1
w = sentence[int(queue[0]["id"]) - 1]
features["prev_word"] = w['form']
return features
def extract(stack, queue, graph, feature_names, sentence):
"""
Returns a row
"""
features = list()
# TODO: Should we use postag os cpostag?
POS_TAG = 'postag'
WORD_TAG = 'form'
ID_TAG = 'id'
DEPREL_TAG = 'deprel'
NULL_VALUE = 'nil'
HEAD_TAG = 'head'
# stack_0
if stack:
stack_0_POS = stack[0][POS_TAG]
stack_0_word = stack[0][WORD_TAG]
else:
stack_0_POS = NULL_VALUE
stack_0_word = NULL_VALUE
# stack_1
if len(stack) > 1:
stack_1_POS = stack[1][POS_TAG]
stack_1_word = stack[1][WORD_TAG]
else:
stack_1_POS = NULL_VALUE
stack_1_word = NULL_VALUE
# queue_0
if queue:
queue_0_POS = queue[0][POS_TAG]
queue_0_word = queue[0][WORD_TAG]
else:
queue_0_POS = NULL_VALUE
queue_0_word = NULL_VALUE
# queue_1
if len(queue) > 1:
queue_1_POS = queue[1][POS_TAG]
queue_1_word = queue[1][WORD_TAG]
else:
queue_1_POS = NULL_VALUE
queue_1_word = NULL_VALUE
if len(feature_names) == 6:
features.append(stack_0_word)
features.append(stack_0_POS)
features.append(queue_0_word)
features.append(queue_0_POS)
features.append(transition.can_reduce(stack, graph))
features.append(transition.can_leftarc(stack, graph))
elif len(feature_names) == 10:
features.append(stack_0_word)
features.append(stack_0_POS)
features.append(stack_1_word)
features.append(stack_1_POS)
features.append(queue_0_word)
features.append(queue_0_POS)
features.append(queue_1_word)
features.append(queue_1_POS)
features.append(transition.can_reduce(stack, graph))
features.append(transition.can_leftarc(stack, graph))
elif len(feature_names) == 13:
# word after top of stack in sentence
if stack_0_word == NULL_VALUE:
after_stack_0_word = NULL_VALUE
after_stack_0_POS = NULL_VALUE
else:
id_stack_0 = int(stack[0]['id'])
if len(sentence) - 1 == id_stack_0: #stack 0 is the last word
after_stack_0_word = NULL_VALUE
after_stack_0_POS = NULL_VALUE
else:
next_word = sentence[id_stack_0 + 1]
after_stack_0_word = next_word[WORD_TAG]
after_stack_0_POS = next_word[POS_TAG]
# # Head of stack 0 POS
# if stack:
# head_index_of_stack_0 = stack[0][HEAD_TAG]
# head_of_stack_0 = sentence[int(head_index_of_stack_0)]
# head_of_stack_0_POS = head_of_stack_0[POS_TAG]
# else:
# head_of_stack_0_POS = NULL_VALUE
features.append(stack_0_word)
features.append(stack_0_POS)
features.append(stack_1_word)
features.append(stack_1_POS)
features.append(queue_0_word)
features.append(queue_0_POS)
features.append(queue_1_word)
features.append(queue_1_POS)
features.append(after_stack_0_word)
features.append(after_stack_0_POS)
features.append(transition.can_reduce(stack, graph))
features.append(transition.can_leftarc(stack, graph))
# Our own features
features.append(transition.can_rightarc(stack))
# features.append(head_of_stack_0_POS)
# Convert features object
features = dict(zip(feature_names, features))
return features
def extract_features_sent(sentence, feature_names):
"""
Extract the features from one sentence
returns X and y, where X is a list of dictionaries and
y is a list of symbols
:param sentence:
:param feature_names
:return:
"""
#sentence = sentence.splitlines()
stack = []
graph = {}
queue = list(sentence)
graph['heads'] = {}
graph['heads']['0'] = '0'
graph['deprels'] = {}
graph['deprels']['0'] = 'ROOT'
transitions = []
x = list()
X = list()
y = list()
while queue:
if (len(stack) > 0):
x.append(stack[0]['cpostag'])
else:
x.append('nil')
if (len(stack) > 1):
x.append(stack[1]['cpostag'])
else:
x.append('nil')
if (len(stack) > 0):
x.append(stack[0]['form'])
else:
x.append('nil')
if (len(stack) > 1):
x.append(stack[1]['form'])
else:
x.append('nil')
if (queue):
x.append(queue[0]['cpostag'])
else:
x.append('nil')
if (len(queue) > 1):
x.append(queue[1]['cpostag'])
else:
x.append('nil')
if (queue):
x.append(queue[0]['form'])
else:
x.append('nil')
if (len(queue) > 1):
x.append(queue[1]['form'])
else:
x.append('nil')
x.append(transition.can_reduce(stack, graph))
x.append(transition.can_leftarc(stack, graph))
X.append(dict(zip(feature_names, x)))
stack, queue, graph, trans = dparser.reference(stack, queue, graph)
y.append(trans)
x = list()
# x.append(stack[0]['cpostag'])
return X, y
def extract_3(stack, queue, graph, feature_names, sentence):
stack_list = ["nil", "nil", "nil", "nil"]
if stack:
stack_list[0] = stack[0]['form']
stack_list[1] = stack[0]['postag']
if len(stack) > 1:
stack_list[2] = stack[1]['form']
stack_list[3] = stack[1]['postag']
queue_list = ["nil", "nil", "nil", "nil"]
if queue:
queue_list[0] = queue[0]['form']
queue_list[1] = queue[0]['postag']
if len(queue) > 1:
queue_list[2] = queue[1]['form']
queue_list[3] = queue[1]['postag']
features = stack_list + queue_list
previous_word = ["nil", "nil"]
if stack:
if int(stack[0]["id"]) > 0:
word = sentence[int(stack[0]["id"]) - 1]
previous_word[0] = word['form']
previous_word[1] = word['postag']
features = features + previous_word
next_word = ["nil", "nil"]
if stack:
if int(stack[0]["id"]) < len(sentence) - 1:
word = sentence[int(stack[0]["id"]) + 1]
next_word[0] = word['form']
next_word[1] = word['postag']
features = features + next_word
can_re = transition.can_reduce(stack, graph)
can_left_arc = transition.can_leftarc(stack, graph)
features.append(can_re)
features.append(can_left_arc)
features = zip(feature_names, features)
features = dict(features)
# print(features)
# print(features)
return features
# extract_features(sentences, w_size, feature_names):
# """
# Builds X matrix and y vector
# X is a list of dictionaries and y is a list
# :param sentences:
# :param w_size:
# :return:
# """
# X_l = []
# y_l = []
# for sentence in sentences:
# X, y = extract_features_sent(sentence, w_size, feature_names, False)
# X_l.extend(X)
# y_l.extend(y)
# return X_l, y_l
#
#
# def extract_features_sent(sentence, w_size, feature_names, useChunk):
# """
# Extract the features from one sentence
# returns X and y, where X is a list of dictionaries and
# y is a list of symbols
# :param sentence: string containing the CoNLL structure of a sentence
# :param w_size:
# :return:
# """
#
# # We pad the sentence to extract the context window more easily
# start = "BOS BOS BOS\n"
# end = "\nEOS EOS EOS"
# start *= w_size
# end *= w_size
# sentence = start + sentence
# sentence += end
#
# # Each sentence is a list of rows
# sentence = sentence.splitlines()
# padded_sentence = list()
# for line in sentence:
# line = line.split()
# padded_sentence.append(line)
# # print(padded_sentence)
#
# # We extract the features and the classes
# # X contains is a list of features, where each feature vector is a dictionary
# # y is the list of classes
# X = list()
# y = list()
# for i in range(len(padded_sentence) - 2 * w_size):
# # x is a row of X
# x = list()
# # The words in lower case
# for j in range(2 * w_size + 1):
# x.append(padded_sentence[i + j][0].lower())
# # The POS
# for j in range(2 * w_size + 1):
# x.append(padded_sentence[i + j][1])
# for j in range(w_size):
# x.append(padded_sentence[i + j][2])
# # for j in range(2):
# # x.append(padded_sentence[i - j + 1][2])
# # The chunks (Up to the word)
# """
# for j in range(w_size):
# feature_line.append(padded_sentence[i + j][2])
# """
# # We represent the feature vector as a dictionary
# X.append(dict(zip(feature_names, x)))
# # The classes are stored in a list
# y.append(padded_sentence[i + w_size][2])
# return X, y
def extract_features_sent(sentence, feature_names, classifier, dict_classes,
vec):
stack = []
graph = {}
queue = list(sentence)
graph['heads'] = {}
graph['heads']['0'] = '0'
graph['deprels'] = {}
graph['deprels']['0'] = 'ROOT'
x = list()
X = list()
d = len(sentence)
while queue:
if (len(stack) > 0):
x.append(stack[0]['cpostag'])
else:
x.append('nil')
if (len(stack) > 1):
x.append(stack[1]['cpostag'])
else:
x.append('nil')
if (len(stack) > 0):
x.append(stack[0]['form'])
else:
x.append('nil')
if (len(stack) > 1):
x.append(stack[1]['form'])
else:
x.append('nil')
if (queue):
x.append(queue[0]['cpostag'])
else:
x.append('nil')
if (len(queue) > 1):
x.append(queue[1]['cpostag'])
else:
x.append('nil')
if (queue):
x.append(queue[0]['form'])
else:
x.append('nil')
if (len(queue) > 1):
x.append(queue[1]['form'])
else:
x.append('nil')
x.append(transition.can_reduce(stack, graph))
x.append(transition.can_leftarc(stack, graph))
X = (dict(zip(feature_names, x)))
trans_nr = classifier.predict(vec.transform(X))[0]
trans = dict_classes[trans_nr]
stack, queue, graph, trans = parse_ml(stack, queue, graph, trans)
x = list()
transition.empty_stack(stack, graph)
for word in sentence:
word['head'] = graph['heads'][word['id']]
word['deprel'] = graph['deprels'][word['id']]
return X
def extract(stack, queue, graph, feature_names, sentence):
features = list()
POS_TAG = "postag"
WORD_TAG = "form"
ID_TAG = "id"
DEPREL_TAG = "deprel"
NULL_VALUE = "nil"
HEAD_TAG = "head"
if stack:
stack_0_pos = stack[0][POS_TAG]
stack_0_word = stack[0][WORD_TAG]
else:
stack_0_pos = NULL_VALUE
stack_0_word = NULL_VALUE
if len(stack) > 1:
stack_1_pos = stack[1][POS_TAG]
stack_1_word = stack[1][WORD_TAG]
else:
stack_1_pos = NULL_VALUE
stack_1_word = NULL_VALUE
if queue:
queue_0_pos = queue[0][POS_TAG]
queue_0_word = queue[0][WORD_TAG]
else:
queue_0_pos = NULL_VALUE
queue_0_word = NULL_VALUE
if len(queue) > 1:
queue_1_pos = queue[1][POS_TAG]
queue_1_word = queue[1][WORD_TAG]
else:
queue_1_pos = NULL_VALUE
queue_1_word = NULL_VALUE
if len(feature_names) == 6:
features.append(stack_0_word)
features.append(stack_0_pos)
features.append(queue_0_word)
features.append(queue_0_pos)
features.append(transition.can_reduce(stack, graph))
features.append(transition.can_leftarc(stack, graph))
elif len(feature_names) == 10:
features.append(stack_0_word)
features.append(stack_0_pos)
features.append(stack_1_word)
features.append(stack_1_pos)
features.append(queue_0_word)
features.append(queue_0_pos)
features.append(queue_1_word)
features.append(queue_1_pos)
features.append(transition.can_reduce(stack, graph))
features.append(transition.can_leftarc(stack, graph))
features = dict(zip(feature_names, features))
return features
def extract(stack, queue, graph, feature_names, sentence):
full_sentence = sentence
features = []
# print(sentence)
try:
#feature_names = [stack_0_postag, stack_0_form,stack_1_postag,stack_1_form,queue_0_postag,queue_0_form_queue_1_postag,queue_1_form_forward_word_postag,forward_word_form_backward_word_postag_backward_word_form]
features.append(stack[0]['postag'])
features.append(stack[0]['form'])
try:
features.append(stack[1]['postag'])
features.append(stack[1]['form'])
except:
features.append("nil")
features.append("nil")
except:
features.append("nil")
features.append("nil")
features.append("nil")
features.append("nil")
try:
features.append(queue[0]['postag'])
features.append(queue[0]['form'])
features.append(queue[1]['postag'])
features.append(queue[1]['form'])
except:
features.append(queue[0]['postag'])
features.append(queue[0]['form'])
features.append("nil")
features.append("nil")
#print(sentence['id'])
try:
id = stack[0]['id']
for ids in range(len(sentence)):
if sentence[ids]['id'] == id:
features.append(sentence[ids + 1]['postag'])
features.append(sentence[ids + 1]['form'])
except:
features.append('nil')
features.append('nil')
try:
id = stack[0]['id']
for ids in range(len(sentence)):
if sentence[ids]['id'] == id:
features.append(sentence[ids - 1]['postag'])
features.append(sentence[ids - 1]['form'])
except:
features.append('nil')
features.append('nil')
if transition.can_leftarc(stack, graph):
can_la = True
else:
can_la = False
if transition.can_reduce(stack, graph):
can_re = True
else:
can_re = False
features.append(can_re)
features.append(can_la)
return features
def extract(stack, queue, graph, feature_names, sentence):
features = list()
POS_TAG = "postag"
WORD_TAG = "form"
ID_TAG = "id"
DEPREL_TAG = "deprel"
NULL_VALUE = "nil"
HEAD_TAG = "head"
PHEAD_TAG = "phead"
PDEPREL_TAG = "pdeprel"
if stack:
stack_0_pos = stack[0][POS_TAG]
stack_0_word = stack[0][WORD_TAG]
else:
stack_0_pos = NULL_VALUE
stack_0_word = NULL_VALUE
if len(stack) > 1:
stack_1_pos = stack[1][POS_TAG]
stack_1_word = stack[1][WORD_TAG]
else:
stack_1_pos = NULL_VALUE
stack_1_word = NULL_VALUE
if queue:
queue_0_pos = queue[0][POS_TAG]
queue_0_word = queue[0][WORD_TAG]
else:
queue_0_pos = NULL_VALUE
queue_0_word = NULL_VALUE
if len(queue) > 1:
queue_1_pos = queue[1][POS_TAG]
queue_1_word = queue[1][WORD_TAG]
else:
queue_1_pos = NULL_VALUE
queue_1_word = NULL_VALUE
if len(feature_names) == 6:
features.append(stack_0_word)
features.append(stack_0_pos)
features.append(queue_0_word)
features.append(queue_0_pos)
features.append(transition.can_reduce(stack, graph))
features.append(transition.can_leftarc(stack, graph))
elif len(feature_names) == 10:
features.append(stack_0_word)
features.append(stack_0_pos)
features.append(stack_1_word)
features.append(stack_1_pos)
features.append(queue_0_word)
features.append(queue_0_pos)
features.append(queue_1_word)
features.append(queue_1_pos)
features.append(transition.can_reduce(stack, graph))
features.append(transition.can_leftarc(stack, graph))
elif len(feature_names) == 14:
# No word
if stack_0_word == NULL_VALUE:
after_stack_0_word = NULL_VALUE
after_stack_0_pos = NULL_VALUE
before_stack_0_word = NULL_VALUE
before_stack_0_pos = NULL_VALUE
else:
id_stack_0 = int(stack[0]['id'])
# Last word
if id_stack_0 == len(sentence) - 1:
after_stack_0_word = NULL_VALUE
after_stack_0_pos = NULL_VALUE
else:
next_word = sentence[id_stack_0 + 1]
after_stack_0_word = next_word[WORD_TAG]
after_stack_0_pos = next_word[POS_TAG]
# First word
if id_stack_0 == 0:
before_stack_0_word = NULL_VALUE
before_stack_0_pos = NULL_VALUE
else:
previous_word = sentence[id_stack_0]
before_stack_0_word = previous_word[WORD_TAG]
before_stack_0_pos = previous_word[POS_TAG]
features.append(stack_0_word)
features.append(stack_0_pos)
features.append(stack_1_word)
features.append(stack_1_pos)
features.append(queue_0_word)
features.append(queue_0_pos)
features.append(queue_1_word)
features.append(queue_1_pos)
features.append(after_stack_0_word)
features.append(after_stack_0_pos)
features.append(before_stack_0_word)
features.append(before_stack_0_pos)
features.append(transition.can_reduce(stack, graph))
features.append(transition.can_leftarc(stack, graph))
features = dict(zip(feature_names, features))
return features
def extract2(stack, queue, state, feature_names, sentence):
"""
Extract the features from one sentence
returns X and y, where X is a list of dictionaries and
y is a list of symbols
:param sentence:
:param w_size:
:return:
"""
tmpsiblings = ['nil', 'nil']
left = 0
right = 1000
if (len(stack) > 0 and len(state) > 0):
#print(state['heads'])
for key, value in (state['heads'].items()):
if (int(key) < int(stack[0]['id'])
and int(value) == int(stack[0]['head'])):
#print(str(key)+' ' + str(value))
#print(stack[0]['id'])
if left < int(key):
left = int(key)
elif int(key) > int(stack[0]['id']) and int(value) == int(
stack[0]['head']):
if right > int(key):
right = int(key)
if right < 1000:
#print('right')
#print(stack[0]['form'])
#print(sentence[right]['form'])
tmpsiblings[1] = sentence[right]['form']
pass
if (left > 0):
#print('left')
#print(stack[0]['form'])
#print(sentence[left]['form'])
tmpsiblings[0] = sentence[left]['form']
pass
#if(indx < stack[0]['id'] and head):
#print(sentence[stack[0]['id']+1])
#while(head > 0):
# print(stack[0]['head'])
x = []
tmpListStack = ['nil', 'nil', 'nil', 'nil']
tmpListQueue = ['nil', 'nil', 'nil', 'nil']
tmpNextWord = ['nil', 'nil']
try:
tmpNextWord[1] = sentence[int(stack[0]['id']) + 1]['postag']
tmpNextWord[1] = sentence[int(stack[0]['id']) + 1]['form']
except:
pass
try:
tmpListStack[0] = stack[0]['form']
tmpListStack[1] = stack[0]['postag']
tmpListStack[2] = stack[1]['form']
tmpListStack[3] = stack[1]['postag']
except:
pass
try:
tmpListQueue[0] = queue[0]['form']
tmpListQueue[1] = queue[0]['postag']
tmpListQueue[2] = queue[1]['form']
tmpListQueue[3] = queue[1]['postag']
except:
pass
x.extend(tmpListStack)
x.extend(tmpListQueue)
x.append(transition.can_rightarc(stack))
x.append(transition.can_reduce(stack, state))
x.extend(tmpNextWord)
x.extend(tmpsiblings)
# We represent the feature vector as a dictionary
# The classes are stored in a list
#y.append(padded_sentence[i + w_size][2])
return dict(zip(feature_names, x))
