NLTK将实体识别命名为Python列表

Question

我使用NLTK ne_chunk从文本中提取命名实体:

my_sent = "WASHINGTON -- In the wake of a string of abuses by New York police officers in the 1990s, Loretta E. Lynch, the top federal prosecutor in Brooklyn, spoke forcefully about the pain of a broken trust that African-Americans felt and said the responsibility for repairing generations of miscommunication and mistrust fell to law enforcement."


nltk.ne_chunk(my_sent, binary=True)

但我无法弄清楚如何将这些实体保存到列表中？例如 -

print Entity_list
('WASHINGTON', 'New York', 'Loretta', 'Brooklyn', 'African')

谢谢.

Answer 1

nltk.ne_chunk返回一个嵌套nltk.tree.Tree对象,因此您必须遍历该Tree对象才能到达NE.

使用正则表达式查看命名实体识别:NLTK

>>> from nltk import ne_chunk, pos_tag, word_tokenize
>>> from nltk.tree import Tree
>>> 
>>> def get_continuous_chunks(text):
...     chunked = ne_chunk(pos_tag(word_tokenize(text)))
...     continuous_chunk = []
...     current_chunk = []
...     for i in chunked:
...             if type(i) == Tree:
...                     current_chunk.append(" ".join([token for token, pos in i.leaves()]))
...             elif 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
... 
>>> my_sent = "WASHINGTON -- In the wake of a string of abuses by New York police officers in the 1990s, Loretta E. Lynch, the top federal prosecutor in Brooklyn, spoke forcefully about the pain of a broken trust that African-Americans felt and said the responsibility for repairing generations of miscommunication and mistrust fell to law enforcement."
>>> get_continuous_chunks(my_sent)
['WASHINGTON', 'New York', 'Loretta E. Lynch', 'Brooklyn']

Answer 2

您还可以label使用以下代码提取文本中每个名称实体的内容:

import nltk
for sent in nltk.sent_tokenize(sentence):
   for chunk in nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sent))):
      if hasattr(chunk, 'label'):
         print(chunk.label(), ' '.join(c[0] for c in chunk))

输出:

GPE WASHINGTON
GPE New York
PERSON Loretta E. Lynch
GPE Brooklyn

你可以看到Washington,New York并且Brooklyn是GPE指地缘政治实体

而且Loretta E. Lynch是一个PERSON

Answer 3

当你得到 atree作为返回值时，我猜你想选择那些标有NE

这是一个简单的示例，用于收集列表中的所有内容：

import nltk

my_sent = "WASHINGTON -- In the wake of a string of abuses by New York police officers in the 1990s, Loretta E. Lynch, the top federal prosecutor in Brooklyn, spoke forcefully about the pain of a broken trust that African-Americans felt and said the responsibility for repairing generations of miscommunication and mistrust fell to law enforcement."

parse_tree = nltk.ne_chunk(nltk.tag.pos_tag(my_sent.split()), binary=True)  # POS tagging before chunking!

named_entities = []

for t in parse_tree.subtrees():
    if t.label() == 'NE':
        named_entities.append(t)
        # named_entities.append(list(t))  # if you want to save a list of tagged words instead of a tree

print named_entities

这给出：

[Tree('NE', [('WASHINGTON', 'NNP')]), Tree('NE', [('New', 'NNP'), ('York', 'NNP')])]

或作为列表列表：

[[('WASHINGTON', 'NNP')], [('New', 'NNP'), ('York', 'NNP')]]

另请参阅：如何导航 nltk.tree.Tree？

Answer 4

使用 nltk.chunk 中的 tree2conlltags。ne_chunk 还需要 pos 标记来标记单词标记（因此需要 word_tokenize）。

from nltk import word_tokenize, pos_tag, ne_chunk
from nltk.chunk import tree2conlltags

sentence = "Mark and John are working at Google."
print(tree2conlltags(ne_chunk(pos_tag(word_tokenize(sentence))
"""[('Mark', 'NNP', 'B-PERSON'), 
    ('and', 'CC', 'O'), ('John', 'NNP', 'B-PERSON'), 
    ('are', 'VBP', 'O'), ('working', 'VBG', 'O'), 
    ('at', 'IN', 'O'), ('Google', 'NNP', 'B-ORGANIZATION'), 
    ('.', '.', 'O')] """

这会给你一个元组列表： [(token, pos_tag, name_entity_tag)] 如果这个列表不是你想要的，那么从这个列表中解析你想要的列表肯定更容易，然后是 nltk 树。

此链接中的代码和详细信息；查看更多信息

您也可以通过仅提取单词来继续，使用以下功能：

def wordextractor(tuple1):

    #bring the tuple back to lists to work with it
    words, tags, pos = zip(*tuple1)
    words = list(words)
    pos = list(pos)
    c = list()
    i=0
    while i<= len(tuple1)-1:
        #get words with have pos B-PERSON or I-PERSON
        if pos[i] == 'B-PERSON':
            c = c+[words[i]]
        elif pos[i] == 'I-PERSON':
            c = c+[words[i]]
        i=i+1

    return c

print(wordextractor(tree2conlltags(nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sentence))))

编辑添加输出文档字符串 **编辑* 仅为 B-Person 添加输出