使用Stanza和CoreNLPClient提取名词短语 [英] Extract Noun Phrases with Stanza and CoreNLPClient

问题描述

我正在尝试使用Stanza(使用Stanford CoreNLP)从句子中提取名词短语.这只能通过Stanza中的CoreNLPClient模块来完成.

I am trying to extract noun phrases from sentences using Stanza(with Stanford CoreNLP). This can only be done with the CoreNLPClient module in Stanza.

# Import client module
from stanza.server import CoreNLPClient
# Construct a CoreNLPClient with some basic annotators, a memory allocation of 4GB, and port number 9001
client = CoreNLPClient(annotators=['tokenize','ssplit','pos','lemma','ner', 'parse'], memory='4G', endpoint='http://localhost:9001')

这是一个句子的示例，我正在客户端中使用tregrex函数来获取所有名词短语. Tregex函数在python中返回dict of dicts.因此，我需要先处理tregrex的输出，然后再将其传递给NLTK中的Tree.fromstring函数，以正确地提取名词短语作为字符串.

Here is an example of a sentence, and I am using the tregrex function in client to get all the noun phrases. Tregex function returns a dict of dicts in python. Thus I needed to process the output of the tregrex before passing it to the Tree.fromstring function in NLTK to correctly extract the Noun phrases as strings.

pattern = 'NP'
text = "Albert Einstein was a German-born theoretical physicist. He developed the theory of relativity."
matches = client.tregrex(text, pattern) ``

因此，我想出了方法stanza_phrases，该方法必须循环遍历dict of dicts，这是tregrex的输出，并正确格式化NLTK中的Tree.fromstring.

Hence, I came up with the method stanza_phrases which has to loop through the dict of dicts which is the output of tregrex and correctly format for Tree.fromstring in NLTK.

def stanza_phrases(matches):
  Nps = []
  for match in matches:
    for items in matches['sentences']:
      for keys,values in items.items():
        s = '(ROOT\n'+ values['match']+')'
        Nps.extend(extract_phrase(s, pattern))
  return set(Nps)

生成要由NLTK使用的树

generates a tree to be used by NLTK

from nltk.tree import Tree
def extract_phrase(tree_str, label):
    phrases = []
    trees = Tree.fromstring(tree_str)
    for tree in trees:
        for subtree in tree.subtrees():
            if subtree.label() == label:
                t = subtree
                t = ' '.join(t.leaves())
                phrases.append(t)

    return phrases

这是我的输出:

{'Albert Einstein', 'He', 'a German-born theoretical physicist', 'relativity',  'the theory', 'the theory of relativity'}

有没有办法让我用更少的行数(尤其是stanza_phrases和extract_phrase方法)来提高代码效率

Is there a way I can make this more code efficient with less number of lines (especially, stanza_phrases and extract_phrase methods)

推荐答案

from stanza.server import CoreNLPClient

# get noun phrases with tregex
def noun_phrases(_client, _text, _annotators=None):
    pattern = 'NP'
    matches = _client.tregex(_text,pattern,annotators=_annotators)
    print("\n".join(["\t"+sentence[match_id]['spanString'] for sentence in matches['sentences'] for match_id in sentence]))

# English example
with CoreNLPClient(timeout=30000, memory='16G') as client:
    englishText = "Albert Einstein was a German-born theoretical physicist. He developed the theory of relativity."
    print('---')
    print(englishText)
    noun_phrases(client,englishText,_annotators="tokenize,ssplit,pos,lemma,parse")

# French example
with CoreNLPClient(properties='french', timeout=30000, memory='16G') as client:
    frenchText = "Je suis John."
    print('---')
    print(frenchText)
    noun_phrases(client,frenchText,_annotators="tokenize,ssplit,mwt,pos,lemma,parse")

这篇关于使用Stanza和CoreNLPClient提取名词短语的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！