本文整理匯總了Python中nltk.parse.dependencygraph.DependencyGraph.tree方法的典型用法代碼示例。如果您正苦於以下問題：Python DependencyGraph.tree方法的具體用法？Python DependencyGraph.tree怎麽用？Python DependencyGraph.tree使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類nltk.parse.dependencygraph.DependencyGraph的用法示例。
在下文中一共展示了DependencyGraph.tree方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: parse
# 需要導入模塊: from nltk.parse.dependencygraph import DependencyGraph [as 別名]
# 或者: from nltk.parse.dependencygraph.DependencyGraph import tree [as 別名]
def parse(self, tokens):
"""
Parses the list of tokens subject to the projectivity constraint
and the productions in the parser's grammar. This uses a method
similar to the span-concatenation algorithm defined in Eisner (1996).
It returns the most probable parse derived from the parser's
probabilistic dependency grammar.
"""
self._tokens = list(tokens)
chart = []
for i in range(0, len(self._tokens) + 1):
chart.append([])
for j in range(0, len(self._tokens) + 1):
chart[i].append(ChartCell(i, j))
if i == j + 1:
if tokens[i - 1] in self._grammar._tags:
for tag in self._grammar._tags[tokens[i - 1]]:
chart[i][j].add(DependencySpan(i - 1, i, i - 1, [-1], [tag]))
else:
print "No tag found for input token '%s', parse is impossible." % tokens[i - 1]
return []
for i in range(1, len(self._tokens) + 1):
for j in range(i - 2, -1, -1):
for k in range(i - 1, j, -1):
for span1 in chart[k][j]._entries:
for span2 in chart[i][k]._entries:
for newspan in self.concatenate(span1, span2):
chart[i][j].add(newspan)
graphs = []
trees = []
max_parse = None
max_score = 0
for parse in chart[len(self._tokens)][0]._entries:
conll_format = ""
malt_format = ""
for i in range(len(tokens)):
malt_format += "%s\t%s\t%d\t%s\n" % (tokens[i], "null", parse._arcs[i] + 1, "null")
conll_format += "\t%d\t%s\t%s\t%s\t%s\t%s\t%d\t%s\t%s\t%s\n" % (
i + 1,
tokens[i],
tokens[i],
parse._tags[i],
parse._tags[i],
"null",
parse._arcs[i] + 1,
"null",
"-",
"-",
)
dg = DependencyGraph(conll_format)
score = self.compute_prob(dg)
if score > max_score:
max_parse = dg.tree()
max_score = score
return [max_parse, max_score]示例2: parse
# 需要導入模塊: from nltk.parse.dependencygraph import DependencyGraph [as 別名]
# 或者: from nltk.parse.dependencygraph.DependencyGraph import tree [as 別名]
def parse(self, tokens):
"""
Performs a projective dependency parse on the list of tokens using
a chart-based, span-concatenation algorithm similar to Eisner (1996).
:param tokens: The list of input tokens.
:type tokens: list(str)
:return: An iterator over parse trees.
:rtype: iter(Tree)
"""
self._tokens = list(tokens)
chart = []
for i in range(0, len(self._tokens) + 1):
chart.append([])
for j in range(0, len(self._tokens) + 1):
chart[i].append(ChartCell(i, j))
if i == j + 1:
chart[i][j].add(DependencySpan(i - 1, i, i - 1, [-1], ['null']))
for i in range(1, len(self._tokens) + 1):
for j in range(i - 2, -1, -1):
for k in range(i - 1, j, -1):
for span1 in chart[k][j]._entries:
for span2 in chart[i][k]._entries:
for newspan in self.concatenate(span1, span2):
chart[i][j].add(newspan)
for parse in chart[len(self._tokens)][0]._entries:
conll_format = ""
# malt_format = ""
for i in range(len(tokens)):
# malt_format += '%s\t%s\t%d\t%s\n' % (tokens[i], 'null', parse._arcs[i] + 1, 'null')
# conll_format += '\t%d\t%s\t%s\t%s\t%s\t%s\t%d\t%s\t%s\t%s\n' % (i+1, tokens[i], tokens[i], 'null', 'null', 'null', parse._arcs[i] + 1, 'null', '-', '-')
# Modify to comply with the new Dependency Graph requirement (at least must have an root elements)
conll_format += '\t%d\t%s\t%s\t%s\t%s\t%s\t%d\t%s\t%s\t%s\n' % (
i + 1,
tokens[i],
tokens[i],
'null',
'null',
'null',
parse._arcs[i] + 1,
'ROOT',
'-',
'-',
)
dg = DependencyGraph(conll_format)
# if self.meets_arity(dg):
yield dg.tree()示例3: parse
# 需要導入模塊: from nltk.parse.dependencygraph import DependencyGraph [as 別名]
# 或者: from nltk.parse.dependencygraph.DependencyGraph import tree [as 別名]
def parse(self, tokens):
"""
Parses the list of tokens subject to the projectivity constraint
and the productions in the parser's grammar. This uses a method
similar to the span-concatenation algorithm defined in Eisner (1996).
It returns the most probable parse derived from the parser's
probabilistic dependency grammar.
"""
self._tokens = list(tokens)
chart = []
for i in range(0, len(self._tokens) + 1):
chart.append([])
for j in range(0, len(self._tokens) + 1):
chart[i].append(ChartCell(i,j))
if i==j+1:
if tokens[i-1] in self._grammar._tags:
for tag in self._grammar._tags[tokens[i-1]]:
chart[i][j].add(DependencySpan(i-1,i,i-1,[-1], [tag]))
else:
chart[i][j].add(DependencySpan(i-1,i,i-1,[-1], [u'NULL']))
for i in range(1,len(self._tokens)+1):
for j in range(i-2,-1,-1):
for k in range(i-1,j,-1):
for span1 in chart[k][j]._entries:
for span2 in chart[i][k]._entries:
for newspan in self.concatenate(span1, span2):
chart[i][j].add(newspan)
trees = []
max_parse = None
max_score = 0
for parse in chart[len(self._tokens)][0]._entries:
conll_format = ""
malt_format = ""
for i in range(len(tokens)):
malt_format += '%s\t%s\t%d\t%s\n' % (tokens[i], 'null', parse._arcs[i] + 1, 'null')
#conll_format += '\t%d\t%s\t%s\t%s\t%s\t%s\t%d\t%s\t%s\t%s\n' % (i+1, tokens[i], tokens[i], parse._tags[i], parse._tags[i], 'null', parse._arcs[i] + 1, 'null', '-', '-')
# Modify to comply with recent change in dependency graph such that there must be a ROOT element.
conll_format += '\t%d\t%s\t%s\t%s\t%s\t%s\t%d\t%s\t%s\t%s\n' % (i+1, tokens[i], tokens[i], parse._tags[i], parse._tags[i], 'null', parse._arcs[i] + 1, 'ROOT', '-', '-')
dg = DependencyGraph(conll_format)
score = self.compute_prob(dg)
trees.append((score, dg.tree()))
trees.sort(key=lambda e: -e[0])
if trees == []:
trees = [(0.0,Tree(tokens[0],tokens[1:]))]
return ((score,tree) for (score, tree) in trees)