将 NLTK 树叶值作为字符串获取
Getting NLTK tree leaf values as a string
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 returns 已解析的单词列表）
(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。因此在'regression'和','之间有一个space。
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 class (http://www.nltk.org/_modules/nltk/tree.html) 下的方法，但是到目前为止没有运气。
简而言之：
使用Tree.leaves()函数访问解析句子中子树的字符串，即：
VPs_str = [" ".join(vp.leaves()) for vp in list(parsed_sent.subtrees(filter=lambda x: x.label()=='VP'))]
没有正确的方法来访问输入中的真实 VP 字符串，因为斯坦福解析器在解析过程之前对文本进行了标记化，并且 NLTK 没有保留字符串的偏移量 API =(
多头：
这个长答案使得其他 NLTK 用户可以使用 NLTK API 访问 Tree 对象到 Stanford 解析器，它可能不像问题 =)
首先为 NLTK 设置环境变量以访问 Stanford 工具，参见：
- https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software#stanford-tagger-ner-tokenizer-and-parser
- https://gist.github.com/alvations/e1df0ba227e542955a8a
TL;DR:
$ cd
$ wget http://nlp.stanford.edu/software/stanford-parser-full-2015-12-09.zip
$ unzip stanford-parser-full-2015-12-09.zip
$ export STANFORDTOOLSDIR=$HOME
$ export CLASSPATH=$STANFORDTOOLSDIR/stanford-parser-full-2015-12-09/stanford-parser.jar:$STANFORDTOOLSDIR/stanford-parser-full-2015-12-09/stanford-parser-3.6.0-models.jar
为 2015 年 12 月 9 日编译的 Stanford Parser 应用 hack（此 hack 将在 https://github.com/nltk/nltk/pull/1280/files 的前沿版本中过时）：
>>> from nltk.internals import find_jars_within_path
>>> from nltk.parse.stanford import StanfordParser
>>> parser=StanfordParser(model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz")
>>> stanford_dir = parser._classpath[0].rpartition('/')[0]
>>> parser._classpath = tuple(find_jars_within_path(stanford_dir))
现在进行词组提取。
首先，我们解析句子：
>>> sent = "Selected variables by univariate/multivariate analysis, constructed logistic regression, calibrated the low defaults portfolio to benchmark ratings, performed back"
>>> parsed_sent = list(parser.raw_parse(sent))[0]
>>> parsed_sent
Tree('ROOT', [Tree('S', [Tree('NP', [Tree('NP', [Tree('JJ', ['Selected']), Tree('NNS', ['variables'])]), Tree('PP', [Tree('IN', ['by']), Tree('NP', [Tree('JJ', ['univariate/multivariate']), Tree('NN', ['analysis'])])]), Tree(',', [',']), Tree('VP', [Tree('VBN', ['constructed']), Tree('NP', [Tree('NP', [Tree('JJ', ['logistic']), Tree('NN', ['regression'])]), Tree(',', [',']), Tree('ADJP', [Tree('VBN', ['calibrated']), Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['low']), Tree('NNS', ['defaults']), Tree('NN', ['portfolio'])]), Tree('PP', [Tree('TO', ['to']), Tree('NP', [Tree('JJ', ['benchmark']), Tree('NNS', ['ratings'])])])])])])]), Tree(',', [','])]), Tree('VP', [Tree('VBD', ['performed']), Tree('ADVP', [Tree('RB', ['back'])])])])])
然后我们遍历树并检查 VP，就像您所做的那样：
>>> VP_tree = list(tree.subtrees(filter=lambda x: x.label()=='VP'))
之后，我们简单地使用子树叶子得到VPs
>>> for vp in VPs:
... print " ".join(vp.leaves())
...
constructed logistic regression , calibrated the low defaults portfolio to benchmark ratings
performed back
因此要获取 VP 字符串：
>>> VPs_str = [" ".join(vp.leaves()) for vp in list(parsed_sent.subtrees(filter=lambda x: x.label()=='VP'))]
>>> VPs_str
[u'constructed logistic regression , calibrated the low defaults portfolio to benchmark ratings', u'performed back']
或者，我个人喜欢使用分块器而不是完整的解析器来提取短语。
使用 nltk_cli 工具 (https://github.com/alvations/nltk_cli):
alvas@ubi:~/git/nltk_cli$ echo "Selected variables by univariate/multivariate analysis, constructed logistic regression, calibrated the low defaults portfolio to benchmark ratings, performed back" > input-doneyo.txt
alvas@ubi:~/git/nltk_cli$ python senna.py --chunk VP input-doneyo.txt calibrated|to benchmark|performed
alvas@ubi:~/git/nltk_cli$ python senna.py --vp input-doneyo.txt
calibrated|to benchmark|performed
alvas@ubi:~/git/nltk_cli$ python senna.py --chunk2 VP+NP input-doneyo.txt
calibrated the low defaults portfolio|to benchmark ratings
VP 标签的输出由 | 分隔，即
输出：
calibrated|to benchmark|performed
代表：
- 已校准
- 基准测试
- 执行
并且VP+NP chunks输出也被|分隔，VP和NP被\t分隔，即
输出：
calibrated the low defaults portfolio|to benchmark ratings
表示（VP + NP）：
- 校准 + 低违约投资组合
- 基准 + 评级
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 to 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 和 characterOffsetEnd:
遍历 JSON 输出
>>> 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，因为解析树没有直接 link 到偏移量。只有依赖三元组包含 link 到 link 到偏移量的单词 ID。
要访问 output['sentences'][0]['tokens'] 中的标记和 'after' 和 'before' 键（但遗憾的是不能直接 link 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' '}]
与 NLTK 或 StanfordParser 无关，获取正常阅读文本的一种方法是 "detokenize" 使用 Moses SMT 脚本的输出(https://github.com/moses-smt/mosesdecoder)，例如：
alvas@ubi:~$ wget https://raw.githubusercontent.com/moses-smt/mosesdecoder/master/scripts/tokenizer/detokenizer.perl
--2016-02-13 21:27:12-- https://raw.githubusercontent.com/moses-smt/mosesdecoder/master/scripts/tokenizer/detokenizer.perl
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 23.235.43.133
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|23.235.43.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 12473 (12K) [text/plain]
Saving to: ‘detokenizer.perl’
100%[===============================================================================================================================>] 12,473 --.-K/s in 0s
2016-02-13 21:27:12 (150 MB/s) - ‘detokenizer.perl’ saved [12473/12473]
alvas@ubi:~$ echo "constructed logistic regression , calibrated the low defaults portfolio to benchmark ratings" 2> /tmp/null
constructed logistic regression , calibrated the low defaults portfolio to benchmark ratings
请注意，输出可能与输入不同，但大多数情况下，它会被转换为我们 read/write.
的普通文本
NLTK 中的 detokenizer 正在筹备中，但我们需要一段时间来对其进行编码、测试并将其推送到存储库，请耐心等待（请参阅 https://github.com/nltk/nltk/issues/1214)
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 returns 已解析的单词列表）
(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。因此在'regression'和','之间有一个space。
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 class (http://www.nltk.org/_modules/nltk/tree.html) 下的方法，但是到目前为止没有运气。
简而言之：
使用Tree.leaves()函数访问解析句子中子树的字符串，即：
VPs_str = [" ".join(vp.leaves()) for vp in list(parsed_sent.subtrees(filter=lambda x: x.label()=='VP'))]
没有正确的方法来访问输入中的真实 VP 字符串，因为斯坦福解析器在解析过程之前对文本进行了标记化，并且 NLTK 没有保留字符串的偏移量 API =(
多头：
这个长答案使得其他 NLTK 用户可以使用 NLTK API 访问 Tree 对象到 Stanford 解析器，它可能不像问题 =)
首先为 NLTK 设置环境变量以访问 Stanford 工具，参见：
- https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software#stanford-tagger-ner-tokenizer-and-parser
- https://gist.github.com/alvations/e1df0ba227e542955a8a
TL;DR:
$ cd
$ wget http://nlp.stanford.edu/software/stanford-parser-full-2015-12-09.zip
$ unzip stanford-parser-full-2015-12-09.zip
$ export STANFORDTOOLSDIR=$HOME
$ export CLASSPATH=$STANFORDTOOLSDIR/stanford-parser-full-2015-12-09/stanford-parser.jar:$STANFORDTOOLSDIR/stanford-parser-full-2015-12-09/stanford-parser-3.6.0-models.jar
为 2015 年 12 月 9 日编译的 Stanford Parser 应用 hack（此 hack 将在 https://github.com/nltk/nltk/pull/1280/files 的前沿版本中过时）：
>>> from nltk.internals import find_jars_within_path
>>> from nltk.parse.stanford import StanfordParser
>>> parser=StanfordParser(model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz")
>>> stanford_dir = parser._classpath[0].rpartition('/')[0]
>>> parser._classpath = tuple(find_jars_within_path(stanford_dir))
现在进行词组提取。
首先，我们解析句子：
>>> sent = "Selected variables by univariate/multivariate analysis, constructed logistic regression, calibrated the low defaults portfolio to benchmark ratings, performed back"
>>> parsed_sent = list(parser.raw_parse(sent))[0]
>>> parsed_sent
Tree('ROOT', [Tree('S', [Tree('NP', [Tree('NP', [Tree('JJ', ['Selected']), Tree('NNS', ['variables'])]), Tree('PP', [Tree('IN', ['by']), Tree('NP', [Tree('JJ', ['univariate/multivariate']), Tree('NN', ['analysis'])])]), Tree(',', [',']), Tree('VP', [Tree('VBN', ['constructed']), Tree('NP', [Tree('NP', [Tree('JJ', ['logistic']), Tree('NN', ['regression'])]), Tree(',', [',']), Tree('ADJP', [Tree('VBN', ['calibrated']), Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['low']), Tree('NNS', ['defaults']), Tree('NN', ['portfolio'])]), Tree('PP', [Tree('TO', ['to']), Tree('NP', [Tree('JJ', ['benchmark']), Tree('NNS', ['ratings'])])])])])])]), Tree(',', [','])]), Tree('VP', [Tree('VBD', ['performed']), Tree('ADVP', [Tree('RB', ['back'])])])])])
然后我们遍历树并检查 VP，就像您所做的那样：
>>> VP_tree = list(tree.subtrees(filter=lambda x: x.label()=='VP'))
之后，我们简单地使用子树叶子得到VPs
>>> for vp in VPs:
... print " ".join(vp.leaves())
...
constructed logistic regression , calibrated the low defaults portfolio to benchmark ratings
performed back
因此要获取 VP 字符串：
>>> VPs_str = [" ".join(vp.leaves()) for vp in list(parsed_sent.subtrees(filter=lambda x: x.label()=='VP'))]
>>> VPs_str
[u'constructed logistic regression , calibrated the low defaults portfolio to benchmark ratings', u'performed back']
或者，我个人喜欢使用分块器而不是完整的解析器来提取短语。
使用 nltk_cli 工具 (https://github.com/alvations/nltk_cli):
alvas@ubi:~/git/nltk_cli$ echo "Selected variables by univariate/multivariate analysis, constructed logistic regression, calibrated the low defaults portfolio to benchmark ratings, performed back" > input-doneyo.txt
alvas@ubi:~/git/nltk_cli$ python senna.py --chunk VP input-doneyo.txt calibrated|to benchmark|performed
alvas@ubi:~/git/nltk_cli$ python senna.py --vp input-doneyo.txt
calibrated|to benchmark|performed
alvas@ubi:~/git/nltk_cli$ python senna.py --chunk2 VP+NP input-doneyo.txt
calibrated the low defaults portfolio|to benchmark ratings
VP 标签的输出由 | 分隔，即
输出：
calibrated|to benchmark|performed
代表：
- 已校准
- 基准测试
- 执行
并且VP+NP chunks输出也被|分隔，VP和NP被\t分隔，即
输出：
calibrated the low defaults portfolio|to benchmark ratings
表示（VP + NP）：
- 校准 + 低违约投资组合
- 基准 + 评级
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 to 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 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，因为解析树没有直接 link 到偏移量。只有依赖三元组包含 link 到 link 到偏移量的单词 ID。
要访问 output['sentences'][0]['tokens'] 中的标记和 'after' 和 'before' 键（但遗憾的是不能直接 link 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' '}]
与 NLTK 或 StanfordParser 无关，获取正常阅读文本的一种方法是 "detokenize" 使用 Moses SMT 脚本的输出(https://github.com/moses-smt/mosesdecoder)，例如：
alvas@ubi:~$ wget https://raw.githubusercontent.com/moses-smt/mosesdecoder/master/scripts/tokenizer/detokenizer.perl
--2016-02-13 21:27:12-- https://raw.githubusercontent.com/moses-smt/mosesdecoder/master/scripts/tokenizer/detokenizer.perl
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 23.235.43.133
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|23.235.43.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 12473 (12K) [text/plain]
Saving to: ‘detokenizer.perl’
100%[===============================================================================================================================>] 12,473 --.-K/s in 0s
2016-02-13 21:27:12 (150 MB/s) - ‘detokenizer.perl’ saved [12473/12473]
alvas@ubi:~$ echo "constructed logistic regression , calibrated the low defaults portfolio to benchmark ratings" 2> /tmp/null
constructed logistic regression , calibrated the low defaults portfolio to benchmark ratings
请注意，输出可能与输入不同，但大多数情况下，它会被转换为我们 read/write.
的普通文本NLTK 中的 detokenizer 正在筹备中，但我们需要一段时间来对其进行编码、测试并将其推送到存储库，请耐心等待（请参阅 https://github.com/nltk/nltk/issues/1214)