我正在尝试将 Tree 对象中的叶值作为字符串获取。这里的树对象是斯坦福解析器的输出。
这是我的代码:
from nltk.parse import stanford
Parser = stanford.StanfordParser("path")
example = "Selected variables by univariate/multivariate analysis, constructed logistic regression, calibrated the low defaults portfolio to benchmark ratings, performed back"
sentences = Parser.raw_parse(example)
for line in sentences:
for sentence in line:
tree = sentence
这就是我提取 VP(动词短语)叶子的方式。
VP=[]
VP_tree = list(tree.subtrees(filter=lambda x: x.label()=='VP'))
for i in VP_tree:
VP.append(' '.join(i.flatten()))
这是 i.flatten() 的样子:(它返回解析后的单词列表)
(VP
constructed
logistic
regression
,
calibrated
the
low
defaults
portfolio
to
benchmark
ratings)
因为我只能将它们作为已解析单词的列表来获取，所以我用“”加入了它们。因此，“回归”和“，”之间有一个空格。
In [33]: VP
Out [33]: [u'constructed logistic regression , calibrated the low defaults portfolio to benchmark ratings']
我想将动词短语作为字符串(而不是作为已解析单词的列表)获取，而不必通过 ' ' 连接它们。
我查看了 Tree 类 ( http://www.nltk.org/_modules/nltk/tree.html ) 下的方法，但到目前为止还没有成功。
请您参考如下方法:
要根据输入位置检索字符串，您应该考虑使用 https://github.com/smilli/py-corenlp而不是 NLTK API 到 Stanford 工具。
首先，您必须下载、安装和设置 Stanford CoreNLP，请参阅 http://stanfordnlp.github.io/CoreNLP/corenlp-server.html#getting-started
然后将 python 包装器安装到 CoreNLP，https://github.com/smilli/py-corenlp
然后，启动服务器后(很多人错过了这一步!)，在python中，你可以这样做:
>>> from pycorenlp import StanfordCoreNLP
>>> stanford = StanfordCoreNLP('http://localhost:9000')
>>> text = ("Selected variables by univariate/multivariate analysis, constructed logistic regression, calibrated the low defaults portfolio to benchmark ratings, performed back")
>>> output = stanford.annotate(text, properties={'annotators': 'tokenize,ssplit,pos,depparse,parse', 'outputFormat': 'json'})
>>> print(output['sentences'][0]['parse'])
(ROOT
(SINV
(VP (VBN Selected)
(NP (NNS variables))
(PP (IN by)
(NP
(NP (JJ univariate/multivariate) (NN analysis))
(, ,)
(VP (VBN constructed)
(NP (JJ logistic) (NN regression)))
(, ,))))
(VP (VBD calibrated))
(NP
(NP
(NP (DT the) (JJ low) (NNS defaults) (NN portfolio))
(PP (TO to)
(NP (JJ benchmark) (NNS ratings))))
(, ,)
(VP (VBN performed)
(ADVP (RB back))))))
要根据输入字符串检索 VP 字符串，您必须使用 characterOffsetBegin 遍历 JSON 输出和 characterOffsetEnd :
>>> output['sentences'][0]
{u'tokens': [{u'index': 1, u'word': u'Selected', u'after': u' ', u'pos': u'VBN', u'characterOffsetEnd': 8, u'characterOffsetBegin': 0, u'originalText': u'Selected', u'before': u''}, {u'index': 2, u'word': u'variables', u'after': u' ', u'pos': u'NNS', u'characterOffsetEnd': 18, u'characterOffsetBegin': 9, u'originalText': u'variables', u'before': u' '}, {u'index': 3, u'word': u'by', u'after': u' ', u'pos': u'IN', u'characterOffsetEnd': 21, u'characterOffsetBegin': 19, u'originalText': u'by', u'before': u' '}, {u'index': 4, u'word': u'univariate/multivariate', u'after': u' ', u'pos': u'JJ', u'characterOffsetEnd': 45, u'characterOffsetBegin': 22, u'originalText': u'univariate/multivariate', u'before': u' '}, {u'index': 5, u'word': u'analysis', u'after': u'', u'pos': u'NN', u'characterOffsetEnd': 54, u'characterOffsetBegin': 46, u'originalText': u'analysis', u'before': u' '}, {u'index': 6, u'word': u',', u'after': u' ', u'pos': u',', u'characterOffsetEnd': 55, u'characterOffsetBegin': 54, u'originalText': u',', u'before': u''}, {u'index': 7, u'word': u'constructed', u'after': u' ', u'pos': u'VBN', u'characterOffsetEnd': 67, u'characterOffsetBegin': 56, u'originalText': u'constructed', u'before': u' '}, {u'index': 8, u'word': u'logistic', u'after': u' ', u'pos': u'JJ', u'characterOffsetEnd': 76, u'characterOffsetBegin': 68, u'originalText': u'logistic', u'before': u' '}, {u'index': 9, u'word': u'regression', u'after': u'', u'pos': u'NN', u'characterOffsetEnd': 87, u'characterOffsetBegin': 77, u'originalText': u'regression', u'before': u' '}, {u'index': 10, u'word': u',', u'after': u' ', u'pos': u',', u'characterOffsetEnd': 88, u'characterOffsetBegin': 87, u'originalText': u',', u'before': u''}, {u'index': 11, u'word': u'calibrated', u'after': u' ', u'pos': u'VBD', u'characterOffsetEnd': 99, u'characterOffsetBegin': 89, u'originalText': u'calibrated', u'before': u' '}, {u'index': 12, u'word': u'the', u'after': u' ', u'pos': u'DT', u'characterOffsetEnd': 103, u'characterOffsetBegin': 100, u'originalText': u'the', u'before': u' '}, {u'index': 13, u'word': u'low', u'after': u' ', u'pos': u'JJ', u'characterOffsetEnd': 107, u'characterOffsetBegin': 104, u'originalText': u'low', u'before': u' '}, {u'index': 14, u'word': u'defaults', u'after': u' ', u'pos': u'NNS', u'characterOffsetEnd': 116, u'characterOffsetBegin': 108, u'originalText': u'defaults', u'before': u' '}, {u'index': 15, u'word': u'portfolio', u'after': u' ', u'pos': u'NN', u'characterOffsetEnd': 126, u'characterOffsetBegin': 117, u'originalText': u'portfolio', u'before': u' '}, {u'index': 16, u'word': u'to', u'after': u' ', u'pos': u'TO', u'characterOffsetEnd': 129, u'characterOffsetBegin': 127, u'originalText': u'to', u'before': u' '}, {u'index': 17, u'word': u'benchmark', u'after': u' ', u'pos': u'JJ', u'characterOffsetEnd': 139, u'characterOffsetBegin': 130, u'originalText': u'benchmark', u'before': u' '}, {u'index': 18, u'word': u'ratings', u'after': u'', u'pos': u'NNS', u'characterOffsetEnd': 147, u'characterOffsetBegin': 140, u'originalText': u'ratings', u'before': u' '}, {u'index': 19, u'word': u',', u'after': u' ', u'pos': u',', u'characterOffsetEnd': 148, u'characterOffsetBegin': 147, u'originalText': u',', u'before': u''}, {u'index': 20, u'word': u'performed', u'after': u' ', u'pos': u'VBN', u'characterOffsetEnd': 158, u'characterOffsetBegin': 149, u'originalText': u'performed', u'before': u' '}, {u'index': 21, u'word': u'back', u'after': u'', u'pos': u'RB', u'characterOffsetEnd': 163, u'characterOffsetBegin': 159, u'originalText': u'back', u'before': u' '}], u'index': 0, u'basic-dependencies': [{u'dep': u'ROOT', u'dependent': 1, u'governorGloss': u'ROOT', u'governor': 0, u'dependentGloss': u'Selected'}, {u'dep': u'dobj', u'dependent': 2, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'variables'}, {u'dep': u'case', u'dependent': 3, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u'by'}, {u'dep': u'amod', u'dependent': 4, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u'univariate/multivariate'}, {u'dep': u'nmod', u'dependent': 5, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'analysis'}, {u'dep': u'punct', u'dependent': 6, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u','}, {u'dep': u'acl', u'dependent': 7, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u'constructed'}, {u'dep': u'amod', u'dependent': 8, u'governorGloss': u'regression', u'governor': 9, u'dependentGloss': u'logistic'}, {u'dep': u'dobj', u'dependent': 9, u'governorGloss': u'constructed', u'governor': 7, u'dependentGloss': u'regression'}, {u'dep': u'punct', u'dependent': 10, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u','}, {u'dep': u'dep', u'dependent': 11, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'calibrated'}, {u'dep': u'det', u'dependent': 12, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'the'}, {u'dep': u'amod', u'dependent': 13, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'low'}, {u'dep': u'compound', u'dependent': 14, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'defaults'}, {u'dep': u'nsubj', u'dependent': 15, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'portfolio'}, {u'dep': u'case', u'dependent': 16, u'governorGloss': u'ratings', u'governor': 18, u'dependentGloss': u'to'}, {u'dep': u'amod', u'dependent': 17, u'governorGloss': u'ratings', u'governor': 18, u'dependentGloss': u'benchmark'}, {u'dep': u'nmod', u'dependent': 18, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'ratings'}, {u'dep': u'punct', u'dependent': 19, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u','}, {u'dep': u'acl', u'dependent': 20, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'performed'}, {u'dep': u'advmod', u'dependent': 21, u'governorGloss': u'performed', u'governor': 20, u'dependentGloss': u'back'}], u'parse': u'(ROOT\n (SINV\n (VP (VBN Selected)\n (NP (NNS variables))\n (PP (IN by)\n (NP\n (NP (JJ univariate/multivariate) (NN analysis))\n (, ,)\n (VP (VBN constructed)\n (NP (JJ logistic) (NN regression)))\n (, ,))))\n (VP (VBD calibrated))\n (NP\n (NP\n (NP (DT the) (JJ low) (NNS defaults) (NN portfolio))\n (PP (TO to)\n (NP (JJ benchmark) (NNS ratings))))\n (, ,)\n (VP (VBN performed)\n (ADVP (RB back))))))', u'collapsed-dependencies': [{u'dep': u'ROOT', u'dependent': 1, u'governorGloss': u'ROOT', u'governor': 0, u'dependentGloss': u'Selected'}, {u'dep': u'dobj', u'dependent': 2, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'variables'}, {u'dep': u'case', u'dependent': 3, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u'by'}, {u'dep': u'amod', u'dependent': 4, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u'univariate/multivariate'}, {u'dep': u'nmod:by', u'dependent': 5, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'analysis'}, {u'dep': u'punct', u'dependent': 6, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u','}, {u'dep': u'acl', u'dependent': 7, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u'constructed'}, {u'dep': u'amod', u'dependent': 8, u'governorGloss': u'regression', u'governor': 9, u'dependentGloss': u'logistic'}, {u'dep': u'dobj', u'dependent': 9, u'governorGloss': u'constructed', u'governor': 7, u'dependentGloss': u'regression'}, {u'dep': u'punct', u'dependent': 10, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u','}, {u'dep': u'dep', u'dependent': 11, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'calibrated'}, {u'dep': u'det', u'dependent': 12, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'the'}, {u'dep': u'amod', u'dependent': 13, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'low'}, {u'dep': u'compound', u'dependent': 14, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'defaults'}, {u'dep': u'nsubj', u'dependent': 15, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'portfolio'}, {u'dep': u'case', u'dependent': 16, u'governorGloss': u'ratings', u'governor': 18, u'dependentGloss': u'to'}, {u'dep': u'amod', u'dependent': 17, u'governorGloss': u'ratings', u'governor': 18, u'dependentGloss': u'benchmark'}, {u'dep': u'nmod:to', u'dependent': 18, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'ratings'}, {u'dep': u'punct', u'dependent': 19, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u','}, {u'dep': u'acl', u'dependent': 20, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'performed'}, {u'dep': u'advmod', u'dependent': 21, u'governorGloss': u'performed', u'governor': 20, u'dependentGloss': u'back'}], u'collapsed-ccprocessed-dependencies': [{u'dep': u'ROOT', u'dependent': 1, u'governorGloss': u'ROOT', u'governor': 0, u'dependentGloss': u'Selected'}, {u'dep': u'dobj', u'dependent': 2, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'variables'}, {u'dep': u'case', u'dependent': 3, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u'by'}, {u'dep': u'amod', u'dependent': 4, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u'univariate/multivariate'}, {u'dep': u'nmod:by', u'dependent': 5, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'analysis'}, {u'dep': u'punct', u'dependent': 6, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u','}, {u'dep': u'acl', u'dependent': 7, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u'constructed'}, {u'dep': u'amod', u'dependent': 8, u'governorGloss': u'regression', u'governor': 9, u'dependentGloss': u'logistic'}, {u'dep': u'dobj', u'dependent': 9, u'governorGloss': u'constructed', u'governor': 7, u'dependentGloss': u'regression'}, {u'dep': u'punct', u'dependent': 10, u'governorGloss': u'analysis', u'governor': 5, u'dependentGloss': u','}, {u'dep': u'dep', u'dependent': 11, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'calibrated'}, {u'dep': u'det', u'dependent': 12, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'the'}, {u'dep': u'amod', u'dependent': 13, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'low'}, {u'dep': u'compound', u'dependent': 14, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'defaults'}, {u'dep': u'nsubj', u'dependent': 15, u'governorGloss': u'Selected', u'governor': 1, u'dependentGloss': u'portfolio'}, {u'dep': u'case', u'dependent': 16, u'governorGloss': u'ratings', u'governor': 18, u'dependentGloss': u'to'}, {u'dep': u'amod', u'dependent': 17, u'governorGloss': u'ratings', u'governor': 18, u'dependentGloss': u'benchmark'}, {u'dep': u'nmod:to', u'dependent': 18, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'ratings'}, {u'dep': u'punct', u'dependent': 19, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u','}, {u'dep': u'acl', u'dependent': 20, u'governorGloss': u'portfolio', u'governor': 15, u'dependentGloss': u'performed'}, {u'dep': u'advmod', u'dependent': 21, u'governorGloss': u'performed', u'governor': 20, u'dependentGloss': u'back'}]}
但它似乎不是一个容易解析的输出以获取字符偏移量，因为没有解析树与偏移量的直接链接。只有依赖三元组包含指向链接到偏移量的单词 ID 的链接。
访问 token 和 'after'和 'before'键入 output['sentences'][0]['tokens'] (但遗憾的是没有直接链接到解析树):
>>> tokens = output['sentences'][0]['tokens']
>>> tokens
[{u'index': 1, u'word': u'Selected', u'after': u' ', u'pos': u'VBN', u'characterOffsetEnd': 8, u'characterOffsetBegin': 0, u'originalText': u'Selected', u'before': u''}, {u'index': 2, u'word': u'variables', u'after': u' ', u'pos': u'NNS', u'characterOffsetEnd': 18, u'characterOffsetBegin': 9, u'originalText': u'variables', u'before': u' '}, {u'index': 3, u'word': u'by', u'after': u' ', u'pos': u'IN', u'characterOffsetEnd': 21, u'characterOffsetBegin': 19, u'originalText': u'by', u'before': u' '}, {u'index': 4, u'word': u'univariate/multivariate', u'after': u' ', u'pos': u'JJ', u'characterOffsetEnd': 45, u'characterOffsetBegin': 22, u'originalText': u'univariate/multivariate', u'before': u' '}, {u'index': 5, u'word': u'analysis', u'after': u'', u'pos': u'NN', u'characterOffsetEnd': 54, u'characterOffsetBegin': 46, u'originalText': u'analysis', u'before': u' '}, {u'index': 6, u'word': u',', u'after': u' ', u'pos': u',', u'characterOffsetEnd': 55, u'characterOffsetBegin': 54, u'originalText': u',', u'before': u''}, {u'index': 7, u'word': u'constructed', u'after': u' ', u'pos': u'VBN', u'characterOffsetEnd': 67, u'characterOffsetBegin': 56, u'originalText': u'constructed', u'before': u' '}, {u'index': 8, u'word': u'logistic', u'after': u' ', u'pos': u'JJ', u'characterOffsetEnd': 76, u'characterOffsetBegin': 68, u'originalText': u'logistic', u'before': u' '}, {u'index': 9, u'word': u'regression', u'after': u'', u'pos': u'NN', u'characterOffsetEnd': 87, u'characterOffsetBegin': 77, u'originalText': u'regression', u'before': u' '}, {u'index': 10, u'word': u',', u'after': u' ', u'pos': u',', u'characterOffsetEnd': 88, u'characterOffsetBegin': 87, u'originalText': u',', u'before': u''}, {u'index': 11, u'word': u'calibrated', u'after': u' ', u'pos': u'VBD', u'characterOffsetEnd': 99, u'characterOffsetBegin': 89, u'originalText': u'calibrated', u'before': u' '}, {u'index': 12, u'word': u'the', u'after': u' ', u'pos': u'DT', u'characterOffsetEnd': 103, u'characterOffsetBegin': 100, u'originalText': u'the', u'before': u' '}, {u'index': 13, u'word': u'low', u'after': u' ', u'pos': u'JJ', u'characterOffsetEnd': 107, u'characterOffsetBegin': 104, u'originalText': u'low', u'before': u' '}, {u'index': 14, u'word': u'defaults', u'after': u' ', u'pos': u'NNS', u'characterOffsetEnd': 116, u'characterOffsetBegin': 108, u'originalText': u'defaults', u'before': u' '}, {u'index': 15, u'word': u'portfolio', u'after': u' ', u'pos': u'NN', u'characterOffsetEnd': 126, u'characterOffsetBegin': 117, u'originalText': u'portfolio', u'before': u' '}, {u'index': 16, u'word': u'to', u'after': u' ', u'pos': u'TO', u'characterOffsetEnd': 129, u'characterOffsetBegin': 127, u'originalText': u'to', u'before': u' '}, {u'index': 17, u'word': u'benchmark', u'after': u' ', u'pos': u'JJ', u'characterOffsetEnd': 139, u'characterOffsetBegin': 130, u'originalText': u'benchmark', u'before': u' '}, {u'index': 18, u'word': u'ratings', u'after': u'', u'pos': u'NNS', u'characterOffsetEnd': 147, u'characterOffsetBegin': 140, u'originalText': u'ratings', u'before': u' '}, {u'index': 19, u'word': u',', u'after': u' ', u'pos': u',', u'characterOffsetEnd': 148, u'characterOffsetBegin': 147, u'originalText': u',', u'before': u''}, {u'index': 20, u'word': u'performed', u'after': u' ', u'pos': u'VBN', u'characterOffsetEnd': 158, u'characterOffsetBegin': 149, u'originalText': u'performed', u'before': u' '}, {u'index': 21, u'word': u'back', u'after': u'', u'pos': u'RB', u'characterOffsetEnd': 163, u'characterOffsetBegin': 159, u'originalText': u'back', u'before': u' '}]
