StanfordCoreNLP中的解析树和Stanza中的解析树给出了不同的结果(表示结构)

Question

我用StanfordCoreNLP做了依赖性解析，用的是下面的代码。

from stanfordcorenlp import StanfordCoreNLP
nlp = StanfordCoreNLP('stanford-corenlp-full-2018-10-05', lang='en')

sentence = 'The clothes in the dressing room are gorgeous. Can I have one?'
tree_str = nlp.parse(sentence)
print(tree_str)

而且我得到了输出。

  (S
    (NP
      (NP (DT The) (NNS clothes))
      (PP (IN in)
        (NP (DT the) (VBG dressing) (NN room))))
    (VP (VBP are)
      (ADJP (JJ gorgeous)))
    (. .)))

我怎么才能在Stanza中得到同样的输出？

import stanza
from stanza.server import CoreNLPClient
classpath='/stanford-corenlp-full-2020-04-20/*'
client = CoreNLPClient(be_quite=False, classpath=classpath, annotators=['parse'], memory='4G', endpoint='http://localhost:8900')
client.start()
text = 'The clothes in the dressing room are gorgeous. Can I have one?'
ann = client.annotate(text)
sentence = ann.sentence[0]
dependency_parse = sentence.basicDependencies
print(dependency_parse)

在Stanza中，我似乎必须把组成句子的句子拆开。我是不是做错了什么？

请注意，我的目标是提取名词短语。

Answer 1

这里有一些关于使用的文档。https:/stanfordnlp.github.iostanzacorenlp_client.html#usage。

这显示了如何获得选区解析（就是你的输出示例的形式）。 依赖解析是一个词之间的边缘列表。

# set up the client
with CoreNLPClient(annotators=['tokenize','ssplit','pos','lemma','ner', 'parse'], timeout=30000, memory='16G') as client:
    # submit the request to the server
    ann = client.annotate(text)

    # get the first sentence
    sentence = ann.sentence[0]

    # get the constituency parse of the first sentence
    print('---')
    print('constituency parse of first sentence')
    constituency_parse = sentence.parseTree
    print(constituency_parse)

    # get the first subtree of the constituency parse
    print('---')
    print('first subtree of constituency parse')
    print(constituency_parse.child[0])