我们从Python开源项目中，提取了以下32个代码示例，用于说明如何使用nltk.parse()。
def __init__(self, meaning, glue, indices=None): if not indices: indices = set() if isinstance(meaning, string_types): self.meaning = Expression.fromstring(meaning) elif isinstance(meaning, Expression): self.meaning = meaning else: raise RuntimeError('Meaning term neither string or expression: %s, %s' % (meaning, meaning.__class__)) if isinstance(glue, string_types): self.glue = linearlogic.LinearLogicParser().parse(glue) elif isinstance(glue, linearlogic.Expression): self.glue = glue else: raise RuntimeError('Glue term neither string or expression: %s, %s' % (glue, glue.__class__)) self.indices = indices
def dep_parse(self, sentence): """ Return a dependency graph for the sentence. :param sentence: the sentence to be parsed :type sentence: list(str) :rtype: DependencyGraph """ #Lazy-initialize the depparser if self.depparser is None: from nltk.parse import MaltParser self.depparser = MaltParser(tagger=self.get_pos_tagger()) if not self.depparser._trained: self.train_depparser() return self.depparser.parse(sentence, verbose=self.verbose)
def __init__(self, meaning, glue, indices=None): if not indices: indices = set() if isinstance(meaning, string_types): self.meaning = drt.DrtExpression.fromstring(meaning) elif isinstance(meaning, drt.DrtExpression): self.meaning = meaning else: raise RuntimeError('Meaning term neither string or expression: %s, %s' % (meaning, meaning.__class__)) if isinstance(glue, string_types): self.glue = linearlogic.LinearLogicParser().parse(glue) elif isinstance(glue, linearlogic.Expression): self.glue = glue else: raise RuntimeError('Glue term neither string or expression: %s, %s' % (glue, glue.__class__)) self.indices = indices
def demo(show_example=-1): from nltk.parse import MaltParser examples = ['David sees Mary', 'David eats a sandwich', 'every man chases a dog', 'every man believes a dog sleeps', 'John gives David a sandwich', 'John chases himself'] # 'John persuades David to order a pizza', # 'John tries to go', # 'John tries to find a unicorn', # 'John seems to vanish', # 'a unicorn seems to approach', # 'every big cat leaves', # 'every gray cat leaves', # 'every big gray cat leaves', # 'a former senator leaves', print('============== DEMO ==============') tagger = RegexpTagger( [('^(David|Mary|John)$', 'NNP'), ('^(sees|eats|chases|believes|gives|sleeps|chases|persuades|tries|seems|leaves)$', 'VB'), ('^(go|order|vanish|find|approach)$', 'VB'), ('^(a)$', 'ex_quant'), ('^(every)$', 'univ_quant'), ('^(sandwich|man|dog|pizza|unicorn|cat|senator)$', 'NN'), ('^(big|gray|former)$', 'JJ'), ('^(him|himself)$', 'PRP') ]) depparser = MaltParser(tagger=tagger) glue = Glue(depparser=depparser, verbose=False) for (i, sentence) in enumerate(examples): if i==show_example or show_example==-1: print('[[[Example %s]]] %s' % (i, sentence)) for reading in glue.parse_to_meaning(sentence.split()): print(reading.simplify()) print('')
def dep_parse(self, sentence): #Lazy-initialize the depparser if self.depparser is None: from nltk.parse import MaltParser self.depparser = MaltParser(tagger=self.get_pos_tagger()) if not self.depparser._trained: self.train_depparser() return [self.depparser.parse(sentence, verbose=self.verbose)]
