如何使用 NLTK ne_chunk 提取 GPE（位置）？

Posted 2023-02-23

【中文标题】如何使用 NLTK ne_chunk 提取 GPE（位置）？

【英文标题】：How can I extract GPE(location) using NLTK ne_chunk?

【发布时间】：2018-02-07 09:46:01

【问题描述】：

我正在尝试使用 OpenWeatherMap API 和 NLTK 执行代码来检查特定区域的天气状况，以查找实体名称识别。但我无法找到将 GPE 中存在的实体（给出位置）（在本例中为芝加哥）传递给我的 API 请求的方法。请帮助我使用语法。下面给出的代码。

感谢您的帮助

import nltk
from nltk import load_parser
import requests
import nltk
from nltk import word_tokenize
from nltk.corpus import stopwords

sentence = "What is the weather in Chicago today? "
tokens = word_tokenize(sentence)

stop_words = set(stopwords.words('english'))

clean_tokens = [w for w in tokens if not w in stop_words]

tagged = nltk.pos_tag(clean_tokens)

print(nltk.ne_chunk(tagged))

【参考方案1】：

GPE 是来自预训练的 ne_chunk 模型的 Tree 对象的标签。

>>> from nltk import word_tokenize, pos_tag, ne_chunk
>>> sent = "What is the weather in Chicago today?"
>>> ne_chunk(pos_tag(word_tokenize(sent)))
Tree('S', [('What', 'WP'), ('is', 'VBZ'), ('the', 'DT'), ('weather', 'NN'), ('in', 'IN'), Tree('GPE', [('Chicago', 'NNP')]), ('today', 'NN'), ('?', '.')])

要遍历树，请参阅How to Traverse an NLTK Tree object?

也许，您正在寻找对NLTK Named Entity recognition to a Python list 稍作修改的东西

from nltk import word_tokenize, pos_tag, ne_chunk
from nltk import Tree

def get_continuous_chunks(text, label):
    chunked = ne_chunk(pos_tag(word_tokenize(text)))
    prev = None
    continuous_chunk = []
    current_chunk = []

    for subtree in chunked:
        if type(subtree) == Tree and subtree.label() == label:
            current_chunk.append(" ".join([token for token, pos in subtree.leaves()]))
        if current_chunk:
            named_entity = " ".join(current_chunk)
            if named_entity not in continuous_chunk:
                continuous_chunk.append(named_entity)
                current_chunk = []
        else:
            continue

    return continuous_chunk

[出]：

>>> sent = "What is the weather in New York today?"
>>> get_continuous_chunks(sent, 'GPE')
['New York']

>>> sent = "What is the weather in New York and Chicago today?"
>>> get_continuous_chunks(sent, 'GPE')
['New York', 'Chicago']

>>> sent = "What is the weather in New York"
>>> get_continuous_chunks(sent, 'GPE')
['New York']

>>> sent = "What is the weather in New York and Chicago"
>>> get_continuous_chunks(sent, 'GPE')
['New York', 'Chicago']

【参考方案2】：

这是解决方案，我想针对您的情况提出建议：

Step 1. Word_tokenize,POS_tagging,Name 实体识别：代码是这样的：

    Xstring = "What is the weather in New York and Chicago today?"

    tokenized_doc  = word_tokenize(Xstring)
    tagged_sentences = nltk.pos_tag(tokenized_doc )
    NE= nltk.ne_chunk(tagged_sentences )
    NE.draw()

Step 2. 名称实体识别后提取所有命名实体（如上）

    named_entities = []
    for tagged_tree in NE:
       print(tagged_tree)
       if hasattr(tagged_tree, 'label'):
          entity_name = ' '.join(c[0] for c in tagged_tree.leaves()) #
          entity_type = tagged_tree.label() # get NE category
          named_entities.append((entity_name, entity_type))

     print(named_entities)  #all entities will be printed,check at your end once

步骤 3.现在只提取 GPE 标签

   for tag in named_entities:
      #print(tag[1])
      if tag[1]=='GPE':   #Specify any tag which is required
        print(tag)

这是我的输出：

  ('New York', 'GPE')
  ('Chicago', 'GPE')