本文整理汇总了Python中nltk.tree.Tree.leaf_treeposition方法的典型用法代码示例。如果您正苦于以下问题：Python Tree.leaf_treeposition方法的具体用法？Python Tree.leaf_treeposition怎么用？Python Tree.leaf_treeposition使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nltk.tree.Tree的用法示例。
在下文中一共展示了Tree.leaf_treeposition方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: traverse
# 需要导入模块: from nltk.tree import Tree [as 别名]
# 或者: from nltk.tree.Tree import leaf_treeposition [as 别名]
def traverse(node):
def extract_tags(W):
pos = [W.getAttribute('lc') if W.getAttribute('lc') else None]
if W.getAttribute('clitic') in {'ezafe', 'pronominal', 'verb', 'prep', 'adv', 'det'}:
pos.append(W.getAttribute('clitic'))
if W.getAttribute('ne_sort'):
pos.append(W.getAttribute('ne_sort'))
if W.getAttribute('n_type'):
pos.append(W.getAttribute('n_type'))
if W.getAttribute('ya_type'):
pos.append(W.getAttribute('ya_type'))
if W.getAttribute('ke_type'):
pos.append(W.getAttribute('ke_type'))
if W.getAttribute('type'):
pos.append(W.getAttribute('type'))
if W.getAttribute('kind'):
pos.append(W.getAttribute('kind'))
return pos
def clitic_join(tree, clitic):
if type(tree[-1]) == Tree:
return clitic_join(tree[-1], clitic)
else:
if(clitic[0][0][0] == 'ا'):
clitic[0] = ('' + clitic[0][0], clitic[0][1])
tree[-1]=(tree[-1][0] + clitic[0][0], clitic[0][1])
tree.set_label('CLITICS')
return
if not len(node.childNodes):
return
first = node.childNodes[0]
if first.tagName == 'w':
pos=extract_tags(first)
return Tree(node.tagName, [(first.childNodes[0].data.replace('می ', 'می'), self._pos_map(pos))])
childs = node.childNodes[2:] if node.tagName == 'S' else node.childNodes
for child in childs:
if not len(child.childNodes):
childs.remove(child)
tree = Tree(node.tagName, map(traverse, childs))
if self._join_clitics and len(tree) > 1 and type(tree[1]) == Tree and tree[1].label() == 'CLITIC' and tree[1][0][1] not in {'P', 'V'}:
clitic=tree[-1]
tree = Tree(tree.label(), [subtree for subtree in tree[0]])
clitic_join(tree, clitic)
if self._join_verb_parts and len(tree) > 1 and type(tree[1]) == Tree and type(tree[0]) == Tree and tree[0].label() == 'AUX' and tree[0][0][0] in self._tokenizer.before_verbs:
tree[1][0] = (tree[0][0][0] + ' ' + tree[1][0][0], tree[1][0][1])
tree.remove(tree[0])
if self._join_verb_parts and len(tree.leaves()) > 1 and tree.leaves()[-1][0] in self._tokenizer.after_verbs and tree.leaves()[-2][0] in self._tokenizer.verbe :
tree[1][0] = (tree[0].leaves()[-1][0] + ' ' + tree[1][0][0], tree[1][0][1])
path = tree.leaf_treeposition(len(tree.leaves())-2)
removingtree = tree
while len(path) > 2 :
removingtree = removingtree[path[0]]
path = path[1:]
removingtree.remove(Tree(tree.pos()[-2][1],[tree.pos()[-2][0]]))
if self._join_verb_parts and len(tree.leaves()) > 1 and tree.leaves()[-1][0] in self._tokenizer.after_verbs and tree.leaves()[-2][0] in self._tokenizer.verbe :
tree[1][0] = (tree[0].leaves()[-1][0] + ' ' + tree[1][0][0], tree[1][0][1])
path = tree.leaf_treeposition(len(tree.leaves())-2)
removingtree = tree
while len(path) > 2 :
removingtree = removingtree[path[0]]
path = path[1:]
removingtree.remove(Tree(tree.pos()[-2][1],[tree.pos()[-2][0]]))
return tree示例2: coref_replace
# 需要导入模块: from nltk.tree import Tree [as 别名]
# 或者: from nltk.tree.Tree import leaf_treeposition [as 别名]
def coref_replace(event_dict, key):
"""
Function to replace pronouns with the referenced noun phrase. Iterates
over each sentence in a news story and pulls coreference information
from the applicable sentence, even if it is from another sentence. Also
keeps track of any changes in indexes made by replacing pronouns, i.e.,
the reference is longer than the reference so the tree index changes for
future references. Filters coreferences on various dimensions to ensure
only "good" coreferences are replaced. The default behavior is to do
no replacement rather than a bad replacement. The function does not
return a value, instead the event_dict is updated with the new parse tree
containing the coref information.
Parameters
----------
event_dict: Dictionary.
Dictionary of sentence information, such as produced by
utilities.parse_sents().
key: String.
ID of the event or news story being processed.
"""
#TODO: This could use some major refactoring.
if 'coref_info' in event_dict[key]['sent_info'].keys():
sent_info = event_dict[key]['sent_info']['sents']
coref_info = event_dict[key]['sent_info']['coref_info']
for sent in coref_info:
for coref in coref_info[sent]['corefs']:
pronoun = coref[0]
ref = coref[1]
if any([word in ref[0] for word in pronoun[0].split()]):
pass
elif any([word in pronoun[0] for word in ref[0].split()]):
pass
elif pronoun[4] - pronoun[3] > 1:
pass
else:
try:
#Getting the stuff for pronouns
if 'coref_tree' in sent_info[pronoun[1]].keys():
pronoun_sent = copy.deepcopy(sent_info[pronoun[1]]
['coref_tree'])
else:
pronoun_sent = copy.deepcopy(sent_info[pronoun[1]]
['parse_tree'])
pronoun_sent = Tree(pronoun_sent)
pro_shift = coref_info[pronoun[1]]['shift']
#Getting stuff for the reference
if 'coref_tree' in sent_info[ref[1]].keys():
coref_sent = sent_info[ref[1]]['coref_tree']
else:
coref_sent = Tree(sent_info[ref[1]]['parse_tree'])
ref_shift = coref_info[ref[1]]['shift']
#Actaully replacing the pronoun
try:
pronoun_pos = pronoun_sent.leaf_treeposition(pronoun[3]
+ pro_shift)
#Hunting for the right pronoun
if pronoun_sent[pronoun_pos] != pronoun[0]:
if pronoun_sent[pronoun_sent.leaf_treeposition(pronoun[3] + (pro_shift - 1))] == pronoun[0]:
pronoun_pos = pronoun_sent.leaf_treeposition(pronoun[3] + (pro_shift - 1))
coref_info[pronoun[1]]['shift'] -= 1
elif pronoun_sent[pronoun_sent.leaf_treeposition(pronoun[3] + (pro_shift + 1))] == pronoun[0]:
pronoun_pos = pronoun_sent.leaf_treeposition(pronoun[3] + (pro_shift + 1))
coref_info[pronoun[1]]['shift'] += 1
else:
break
#Hunting for the right coref
original_coref_index = coref_sent.leaf_treeposition(ref[3])[:-2]
if ' '.join(coref_sent[original_coref_index].leaves()) == ref[0]:
coref_pos = coref_sent.leaf_treeposition(ref[3])[:-2]
elif ref[0] in ' '.join(coref_sent[original_coref_index].leaves()):
coref_pos = coref_sent.leaf_treeposition(ref[3])[:-2]
else:
coref_pos = coref_sent.leaf_treeposition(ref[3] + ref_shift)[:-2]
if ref[0] not in ' '.join(coref_sent[coref_pos].leaves()):
pass
#Found everything, now replace
coref_tree = Tree('COREF', [coref_sent[coref_pos]])
pronoun_sent[pronoun_pos[:-1]] = coref_tree
except IndexError:
#TODO: Should this use the original sentence rather
#than possibly bad coreferences?
print """Key {}, sentence {} has a problem with the corefencing. Breaking and moving on.\n""".format(key, sent)
break
#Recording the shift length for the pronoun replacement
if len(coref_tree.leaves()) <= 2:
coref_info[pronoun[1]]['shift'] += 0
else:
coref_info[pronoun[1]]['shift'] += coref_tree.height()
coref_info[pronoun[1]]['errors'].append(False)
#.........这里部分代码省略.........