python之使用python从文本中提取城市名称_编程设计

我有一个数据集，其中一列的标题是“您所在的位置和时区是什么？”

这意味着我们有类似的条目

丹麦，CET
位置是英格兰德文郡，格林威治标准时间
澳大利亚。澳大利亚东部标准时间。 +10h UTC。

甚至

我一年中的大部分时间都在俄勒冈州尤金或在首尔，韩国视学校假期而定。我的主要时区是太平洋时区。
整个 5 月我都在英国伦敦 (GMT+1)。整个六月，我将在挪威 (GMT+2) 或以色列 (GMT+3) 互联网访问受限。整个七八月份我将在英国伦敦 (GMT+1)。然后从 2015 年 9 月，我将在美国波士顿 (EDT)

有没有办法从中提取城市、国家和时区？

我正在考虑创建一个数组(来自开源数据集)，其中包含所有国家名称(包括缩写形式)以及城市名称/时区，然后如果数据集中的任何单词与城市/国家/时区或缩写形式将其填充到同一数据集中的新列中并对其进行计数。

这实用吗？

=========== 基于 NLTK 答案的回复 ============

运行与我得到的 Alecxe 相同的代码

Traceback (most recent call last): 
  File "E:\SBTF\ntlk_test.py", line 19, in <module> 
    tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences] 
  File "C:\Python27\ArcGIS10.4\lib\site-packages\nltk\tag\__init__.py", line 110, in pos_tag 
    tagger = PerceptronTagger() 
  File "C:\Python27\ArcGIS10.4\lib\site-packages\nltk\tag\perceptron.py", line 141, in __init__ 
    self.load(AP_MODEL_LOC) 
  File "C:\Python27\ArcGIS10.4\lib\site-packages\nltk\tag\perceptron.py", line 209, in load 
    self.model.weights, self.tagdict, self.classes = load(loc) 
  File "C:\Python27\ArcGIS10.4\lib\site-packages\nltk\data.py", line 801, in load 
    opened_resource = _open(resource_url) 
  File "C:\Python27\ArcGIS10.4\lib\site-packages\nltk\data.py", line 924, in _open 
    return urlopen(resource_url) 
  File "C:\Python27\ArcGIS10.4\lib\urllib2.py", line 154, in urlopen 
    return opener.open(url, data, timeout) 
  File "C:\Python27\ArcGIS10.4\lib\urllib2.py", line 431, in open 
    response = self._open(req, data) 
  File "C:\Python27\ArcGIS10.4\lib\urllib2.py", line 454, in _open 
    'unknown_open', req) 
  File "C:\Python27\ArcGIS10.4\lib\urllib2.py", line 409, in _call_chain 
    result = func(*args) 
  File "C:\Python27\ArcGIS10.4\lib\urllib2.py", line 1265, in unknown_open 
    raise URLError('unknown url type: %s' % type) 
URLError: <urlopen error unknown url type: c>

请您参考如下方法:

我会使用自然语言处理和 nltk必须提出提取实体。

示例(主要基于 this gist )标记文件中的每一行，将其拆分为 block 并递归地为每个 block 查找 NE(命名实体)标签。更多解释 here :

import nltk 
 
def extract_entity_names(t): 
    entity_names = [] 
 
    if hasattr(t, 'label') and t.label: 
        if t.label() == 'NE': 
            entity_names.append(' '.join([child[0] for child in t])) 
        else: 
            for child in t: 
                entity_names.extend(extract_entity_names(child)) 
 
    return entity_names 
 
with open('sample.txt', 'r') as f: 
    for line in f: 
        sentences = nltk.sent_tokenize(line) 
        tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences] 
        tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences] 
        chunked_sentences = nltk.ne_chunk_sents(tagged_sentences, binary=True) 
 
        entities = [] 
        for tree in chunked_sentences: 
            entities.extend(extract_entity_names(tree)) 
 
        print(entities)

sample.txt 包含:

Denmark, CET 
Location is Devon, England, GMT time zone 
Australia. Australian Eastern Standard Time. +10h UTC. 
My location is Eugene, Oregon for most of the year or in Seoul, South Korea depending on school holidays. My primary time zone is the Pacific time zone. 
For the entire May I will be in London, United Kingdom (GMT+1). For the entire June I will be in either Norway (GMT+2) or Israel (GMT+3) with limited internet access. For the entire July and August I will be in London, United Kingdom (GMT+1). And then from September, 2015, I will be in Boston, United States (EDT)

它打印:

['Denmark', 'CET'] 
['Location', 'Devon', 'England', 'GMT'] 
['Australia', 'Australian Eastern Standard Time'] 
['Eugene', 'Oregon', 'Seoul', 'South Korea', 'Pacific'] 
['London', 'United Kingdom', 'Norway', 'Israel', 'London', 'United Kingdom', 'Boston', 'United States', 'EDT']

输出并不理想，但对您来说可能是一个好的开始。