如果我的文字中包含例如加泰罗尼亚语报纸的文章，我怎样才能从该文本中找到所有城市？
我一直在查看py包的nltk包，我已经下载了加泰罗尼亚语语料库（nltk.corpus.cess_cat）。
此时我所拥有的： 我从nltk.download（）安装了所有必要的东西。我现在所拥有的一个例子：
te = nltk.word_tokenize('Tots els gats son de Sant Cugat del Valles.')
nltk.pos_tag(te)
这座城市是'Sant Cugat del Valles'。我从输出中获得的是：
[('Tots', 'NNS'),
('els', 'NNS'),
('gats', 'NNS'),
('son', 'VBP'),
('de', 'IN'),
('Sant', 'NNP'),
('Cugat', 'NNP'),
('del', 'NN'),
('Valles', 'NNP')]
NNP似乎表示名字首字母为大写的名词。有没有办法获得地方或城市，而不是所有的名字？ 谢谢
答案 0 :(得分：22)
您可以使用geotext python库。
pip install geotext
只需安装此库即可。用法很简单：
from geotext import GeoText
places = GeoText("London is a great city")
places.cities
给出结果'伦敦'
这个图书馆所涵盖的城市名单并不广泛，但它有一个很好的清单。
答案 1 :(得分：5)
您不需要使用NLTK。相反，请执行以下操作：
以列表形式迭代文本中的元素。
3.1。如果元素第一个元素与文本中的元素对应，则遍历城市，然后检查下一个元素。
以下是可运行的代码示例：
text = 'Tots els gats son de Sant Cugat del Valles.'
#Prepare your text. Remove "." (and other unnecessary marks).
#Then split it into a list of words.
text = text.replace('.','').split(' ')
#Insert the cities you want to search for.
cities = {"Sant Cugat del Valles":["Sant","Cugat","del","Valles"]}
found_match = False
for word in text:
if found_match:
cityTest = cityTest
else:
cityTest = ''
found_match = False
for city in cities.keys():
if word in cities[city]:
cityTest += word + ' '
found_match = True
if cityTest.split(' ')[0:-1] == city.split(' '):
print city #Print if it found a city.
答案 2 :(得分：5)
您要么训练命名实体识别器（NER），要么制作自己的地名录。
我为这样的任务制作并使用了一个简单的地名词典就是这个：
# -*- coding: utf-8 -*-
import codecs
from lxml.html.builder import DT
import os
import re
from nltk.chunk.util import conlltags2tree
from nltk.chunk import ChunkParserI
from nltk.tag import pos_tag
from nltk.tokenize import wordpunct_tokenize
def sub_leaves(tree, node):
return [t.leaves() for t in tree.subtrees(lambda s: s.node == node)]
class Gazetteer(ChunkParserI):
"""
Find and annotate a list of words that matches patterns.
Patterns may be regular expressions in the form list of tuples.
Every tuple has the regular expression and the iob tag for this one.
Before applying gazetteer words a part of speech tagging should
be performed. So, you have to pass your tagger as a parameter.
Example:
>>> patterns = [(u"Αθήνα[ς]?", "LOC"), (u"Νομική[ς]? [Σσ]χολή[ς]?", "ORG")]
>>> gazetteer = Gazetteer(patterns, nltk.pos_tag, nltk.wordpunct_tokenize)
>>> text = u"Η Νομική σχολή της Αθήνας"
>>> t = gazetteer.parse(text)
>>> print(unicode(t))
... (S Η/DT (ORG Νομική/NN σχολή/NN) της/DT (LOC Αθήνας/NN))
"""
def __init__(self, patterns, pos_tagger, tokenizer):
"""
Initialize the class.
:param patterns:
The patterns to search in text is a list of tuples with regular
expression and the tag to apply
:param pos_tagger:
The tagger to use for applying part of speech to the text
:param tokenizer:
The tokenizer to use for tokenizing the text
"""
self.patterns = patterns
self.pos_tag = pos_tagger
self.tokenize = tokenizer
self.lookahead = 0 # how many words it is possible to be a gazetteer word
self.words = [] # Keep the words found by applying the regular expressions
self.iobtags = [] # For each set of words keep the coresponding tag
def iob_tags(self, tagged_sent):
"""
Search the tagged sentences for gazetteer words and apply their iob tags.
:param tagged_sent:
A tokenized text with part of speech tags
:type tagged_sent: list
:return:
yields the IOB tag of the word with it's character, eg. B-LOCATION
:rtype:
"""
i = 0
l = len(tagged_sent)
inside = False # marks the I- tag
iobs = []
while i < l:
word, pos_tag = tagged_sent[i]
j = i + 1 # the next word
k = j + self.lookahead # how many words in a row we may search
nextwords, nexttags = [], [] # for now, just the ith word
add_tag = False # no tag, this is O
while j <= k:
words = ' '.join([word] + nextwords) # expand our word list
if words in self.words: # search for words
index = self.words.index(words) # keep index to use for iob tags
if inside:
iobs.append((word, pos_tag, 'I-' + self.iobtags[index])) # use the index tag
else:
iobs.append((word, pos_tag, 'B-' + self.iobtags[index]))
for nword, ntag in zip(nextwords, nexttags): # there was more than one word
iobs.append((nword, ntag, 'I-' + self.iobtags[index])) # apply I- tag to all of them
add_tag, inside = True, True
i = j # skip tagged words
break
if j < l: # we haven't reach the length of tagged sentences
nextword, nexttag = tagged_sent[j] # get next word and it's tag
nextwords.append(nextword)
nexttags.append(nexttag)
j += 1
else:
break
if not add_tag: # unkown words
inside = False
i += 1
iobs.append((word, pos_tag, 'O')) # it's an Outsider
return iobs
def parse(self, text, conlltags=True):
"""
Given a text, applies tokenization, part of speech tagging and the
gazetteer words with their tags. Returns an conll tree.
:param text: The text to parse
:type text: str
:param conlltags:
:type conlltags:
:return: An conll tree
:rtype:
"""
# apply the regular expressions and find all the
# gazetteer words in text
for pattern, tag in self.patterns:
words_found = set(re.findall(pattern, text)) # keep the unique words
if len(words_found) > 0:
for word in words_found: # words_found may be more than one
self.words.append(word) # keep the words
self.iobtags.append(tag) # and their tag
# find the pattern with the maximum words.
# this will be the look ahead variable
for word in self.words: # don't care about tags now
nwords = word.count(' ')
if nwords > self.lookahead:
self.lookahead = nwords
# tokenize and apply part of speech tagging
tagged_sent = self.pos_tag(self.tokenize(text))
# find the iob tags
iobs = self.iob_tags(tagged_sent)
if conlltags:
return conlltags2tree(iobs)
else:
return iobs
if __name__ == "__main__":
patterns = [(u"Αθήνα[ς]?", "LOC"), (u"Νομική[ς]? [Σσ]χολή[ς]?", "ORG")]
g = Gazetteer(patterns, pos_tag, wordpunct_tokenize)
text = u"Η Νομική σχολή της Αθήνας"
t = g.parse(text)
print(unicode(t))
dir_with_lists = "Lists"
patterns = []
tags = []
for root, dirs, files in os.walk(dir_with_lists):
for f in files:
lines = codecs.open(os.path.join(root, f), 'r', 'utf-8').readlines()
tag = os.path.splitext(f)[0]
for l in lines[1:]:
patterns.append((l.rstrip(), tag))
tags.append(tag)
text = codecs.open("sample.txt", 'r', "utf-8").read()
#g = Gazetteer(patterns)
t = g.parse(text.lower())
print unicode(t)
for tag in set(tags):
for gaz_word in sub_leaves(t, tag):
print gaz_word[0][0], tag
在if __name__ == "__main__":中，您可以看到我在代码patterns = [(u"Αθήνα[ς]?", "LOC"), (u"Νομική[ς]? [Σσ]χολή[ς]?", "ORG")]中制作模式的示例。
稍后在代码中，我从名为Lists的目录中读取文件（将其放在您拥有上述代码的文件夹中）。每个文件的名称都成为Gazetteer的标签。因此，像LOC.txt这样的文件包含位置模式（LOC标记），人员PERSON.txt等等。