从Stanford解析器的无上下文短语结构输出中提取信息

Question

斯坦福解析器（http://nlp.stanford.edu/software/lex-parser.shtml）生成上下文无关短语结构树，如下所示。有没有最好的方法来提取像所有名词短语（NP）和动词短语（NP）在树中一样的东西？是否有任何Python（或Java）库可以让我读取这些结构？谢谢。

(ROOT
  (S
    (S
      (NP
        (NP (DT The) (JJS strongest) (NN rain))
        (VP
          (ADVP (RB ever))
          (VBN recorded)
          (PP (IN in)
            (NP (NNP India)))))
      (VP
        (VP (VBD shut)
          (PRT (RP down))
          (NP
            (NP (DT the) (JJ financial) (NN hub))
            (PP (IN of)
              (NP (NNP Mumbai)))))
        (, ,)
        (VP (VBD snapped)
          (NP (NN communication) (NNS lines)))
        (, ,)
        (VP (VBD closed)
          (NP (NNS airports)))
        (CC and)
        (VP (VBD forced)
          (NP
            (NP (NNS thousands))
            (PP (IN of)
              (NP (NNS people))))
          (S
            (VP (TO to)
              (VP
                (VP (VB sleep)
                  (PP (IN in)
                    (NP (PRP$ their) (NNS offices))))
                (CC or)
                (VP (VB walk)
                  (NP (NN home))
                  (PP (IN during)
                    (NP (DT the) (NN night))))))))))
    (, ,)
    (NP (NNS officials))
    (VP (VBD said)
      (NP-TMP (NN today)))
    (. .)))

- Feng Mai

Answer 1

请查看自然语言工具包（NLTK）网站：nltk.org。该工具包使用Python编写，提供了读取这种类型树形结构的代码（以及其他很多东西）。

或者，您可以编写自己的递归函数来完成此任务。这将非常简单。

仅供娱乐：以下是一个超级简单的实现示例：

def parse():
  itr = iter(filter(lambda x: x, re.split("\\s+", s.replace('(', ' ( ').replace(')', ' ) '))))

  def _parse():
    stuff = []
    for x in itr:
      if x == ')':
        return stuff
      elif x == '(':
        stuff.append(_parse())
      else:
        stuff.append(x)
    return stuff

  return _parse()[0]

def find(parsed, tag):
  if parsed[0] == tag:
    yield parsed
  for x in parsed[1:]:
    for y in find(x, tag):
      yield y

p = parse()
np = find(p, 'NP')
for x in np:
  print x

产生：

['NP', ['NP', ['DT', 'The'], ['JJS', 'strongest'], ['NN', 'rain']], ['VP', ['ADVP', ['RB', 'ever']], ['VBN', 'recorded'], ['PP', ['IN', 'in'], ['NP', ['NNP', 'India']]]]]
['NP', ['DT', 'The'], ['JJS', 'strongest'], ['NN', 'rain']]
['NP', ['NNP', 'India']]
['NP', ['NP', ['DT', 'the'], ['JJ', 'financial'], ['NN', 'hub']], ['PP', ['IN', 'of' ['NP', ['NNP', 'Mumbai']]]]
['NP', ['DT', 'the'], ['JJ', 'financial'], ['NN', 'hub']]
['NP', ['NNP', 'Mumbai']]
['NP', ['NN', 'communication'], ['NNS', 'lines']]
['NP', ['NNS', 'airports']]
['NP', ['NP', ['NNS', 'thousands']], ['PP', ['IN', 'of'], ['NP', ['NNS', 'people']]]]
['NP', ['NNS', 'thousands']]
['NP', ['NNS', 'people']]
['NP', ['PRP$', 'their'], ['NNS', 'offices']]
['NP', ['NN', 'home']]
['NP', ['DT', 'the'], ['NN', 'night']]
['NP', ['NNS', 'officials']]

- dhg