使用 nltk 改进人名的提取 [关闭]

Question

我正在尝试从文本中提取人名。

有没有人推荐的方法？

这是我尝试过的（代码如下）： 我正在使用nltk 查找标记为人的所有内容，然后生成该人所有 NNP 部分的列表。我跳过只有一个 NNP 的人，以避免抓住一个单独的姓氏。

我得到了不错的结果，但想知道是否有更好的方法来解决这个问题。

代码：

import nltk
from nameparser.parser import HumanName

def get_human_names(text):
    tokens = nltk.tokenize.word_tokenize(text)
    pos = nltk.pos_tag(tokens)
    sentt = nltk.ne_chunk(pos, binary = False)
    person_list = []
    person = []
    name = ""
    for subtree in sentt.subtrees(filter=lambda t: t.node == 'PERSON'):
        for leaf in subtree.leaves():
            person.append(leaf[0])
        if len(person) > 1: #avoid grabbing lone surnames
            for part in person:
                name += part + ' '
            if name[:-1] not in person_list:
                person_list.append(name[:-1])
            name = ''
        person = []

    return (person_list)

text = """
Some economists have responded positively to Bitcoin, including 
Francois R. Velde, senior economist of the Federal Reserve in Chicago 
who described it as "an elegant solution to the problem of creating a 
digital currency." In November 2013 Richard Branson announced that 
Virgin Galactic would accept Bitcoin as payment, saying that he had invested 
in Bitcoin and found it "fascinating how a whole new global currency 
has been created", encouraging others to also invest in Bitcoin.
Other economists commenting on Bitcoin have been critical. 
Economist Paul Krugman has suggested that the structure of the currency 
incentivizes hoarding and that its value derives from the expectation that 
others will accept it as payment. Economist Larry Summers has expressed 
a "wait and see" attitude when it comes to Bitcoin. Nick Colas, a market 
strategist for ConvergEx Group, has remarked on the effect of increasing 
use of Bitcoin and its restricted supply, noting, "When incremental 
adoption meets relatively fixed supply, it should be no surprise that 
prices go up. And that’s exactly what is happening to BTC prices."
"""

names = get_human_names(text)
print "LAST, FIRST"
for name in names: 
    last_first = HumanName(name).last + ', ' + HumanName(name).first
        print last_first

输出：

LAST, FIRST
Velde, Francois
Branson, Richard
Galactic, Virgin
Krugman, Paul
Summers, Larry
Colas, Nick

除了维珍银河，这都是有效的输出。当然，在本文的上下文中，知道维珍银河不是人名是困难的（也许是不可能的）部分。

Answer 1

必须同意“让我的代码更好”的建议不太适合这个网站，但我可以给你一些方法让你尝试深入了解。

看看Stanford Named Entity Recognizer (NER)。它的绑定已包含在 NLTK v 2.0 中，但您必须下载一些核心文件。这是script，它可以为您完成所有这些工作。

我写了这个脚本：

import nltk
from nltk.tag.stanford import NERTagger
st = NERTagger('stanford-ner/all.3class.distsim.crf.ser.gz', 'stanford-ner/stanford-ner.jar')
text = """YOUR TEXT GOES HERE"""

for sent in nltk.sent_tokenize(text):
    tokens = nltk.tokenize.word_tokenize(sent)
    tags = st.tag(tokens)
    for tag in tags:
        if tag[1]=='PERSON': print tag

并得到了不错的输出：

('弗朗索瓦', '人') （'R.'，'人'） （'Velde'，'人'） （“理查德”，“人”） （“布兰森”，“人”） （“处女”，“人”） （“银河”，“人”） （“比特币”、“人”） （“比特币”、“人”） （“保罗”，“人”） （“克鲁格曼”，“人”） （“拉里”，“人”） （“夏天”，“人”） （“比特币”、“人”） （“尼克”，“人”） （“可乐”，“人”）

希望这有帮助。

Answer 2

对于其他人来说，我发现这篇文章很有用：http://timmcnamara.co.nz/post/2650550090/extracting-names-with-6-lines-of-python-code

>>> import nltk
>>> def extract_entities(text):
...     for sent in nltk.sent_tokenize(text):
...         for chunk in nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sent))):
...             if hasattr(chunk, 'node'):
...                 print chunk.node, ' '.join(c[0] for c in chunk.leaves())
...

Answer 3

我实际上只想提取人名，因此，我想检查所有作为 wordnet 输出的名称（一个大型的英语词汇数据库）。 更多关于 Wordnet 的信息可以在这里找到：http://www.nltk.org/howto/wordnet.html

import nltk
from nameparser.parser import HumanName
from nltk.corpus import wordnet


person_list = []
person_names=person_list
def get_human_names(text):
    tokens = nltk.tokenize.word_tokenize(text)
    pos = nltk.pos_tag(tokens)
    sentt = nltk.ne_chunk(pos, binary = False)

    person = []
    name = ""
    for subtree in sentt.subtrees(filter=lambda t: t.label() == 'PERSON'):
        for leaf in subtree.leaves():
            person.append(leaf[0])
        if len(person) > 1: #avoid grabbing lone surnames
            for part in person:
                name += part + ' '
            if name[:-1] not in person_list:
                person_list.append(name[:-1])
            name = ''
        person = []
#     print (person_list)

text = """

Some economists have responded positively to Bitcoin, including 
Francois R. Velde, senior economist of the Federal Reserve in Chicago 
who described it as "an elegant solution to the problem of creating a 
digital currency." In November 2013 Richard Branson announced that 
Virgin Galactic would accept Bitcoin as payment, saying that he had invested 
in Bitcoin and found it "fascinating how a whole new global currency 
has been created", encouraging others to also invest in Bitcoin.
Other economists commenting on Bitcoin have been critical. 
Economist Paul Krugman has suggested that the structure of the currency 
incentivizes hoarding and that its value derives from the expectation that 
others will accept it as payment. Economist Larry Summers has expressed 
a "wait and see" attitude when it comes to Bitcoin. Nick Colas, a market 
strategist for ConvergEx Group, has remarked on the effect of increasing 
use of Bitcoin and its restricted supply, noting, "When incremental 
adoption meets relatively fixed supply, it should be no surprise that 
prices go up. And that’s exactly what is happening to BTC prices."
"""

names = get_human_names(text)
for person in person_list:
    person_split = person.split(" ")
    for name in person_split:
        if wordnet.synsets(name):
            if(name in person):
                person_names.remove(person)
                break

print(person_names)

输出

['Francois R. Velde', 'Richard Branson', 'Economist Paul Krugman', 'Nick Colas']

除了拉里·萨默斯之外，所有的名字都是正确的，这是因为姓氏“萨默斯”。

Answer 4

@trojane 的回答对我来说不太奏效，但对我有很大帮助。

先决条件

创建一个文件夹stanford-ner，并将以下两个文件下载到其中：

• english.all.3class.distsim.crf.ser.gz
• stanford-ner.jar（查找 download 并提取存档）

脚本

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import nltk
from nltk.tag.stanford import StanfordNERTagger

text = u"""
Some economists have responded positively to Bitcoin, including
Francois R. Velde, senior economist of the Federal Reserve in Chicago
who described it as "an elegant solution to the problem of creating a
digital currency." In November 2013 Richard Branson announced that
Virgin Galactic would accept Bitcoin as payment, saying that he had invested
in Bitcoin and found it "fascinating how a whole new global currency
has been created", encouraging others to also invest in Bitcoin.
Other economists commenting on Bitcoin have been critical.
Economist Paul Krugman has suggested that the structure of the currency
incentivizes hoarding and that its value derives from the expectation that
others will accept it as payment. Economist Larry Summers has expressed
a "wait and see" attitude when it comes to Bitcoin. Nick Colas, a market
strategist for ConvergEx Group, has remarked on the effect of increasing
use of Bitcoin and its restricted supply, noting, "When incremental
adoption meets relatively fixed supply, it should be no surprise that
prices go up. And that’s exactly what is happening to BTC prices.
"""

st = StanfordNERTagger('stanford-ner/english.all.3class.distsim.crf.ser.gz',
                       'stanford-ner/stanford-ner.jar')

for sent in nltk.sent_tokenize(text):
    tokens = nltk.tokenize.word_tokenize(sent)
    tags = st.tag(tokens)
    for tag in tags:
        if tag[1] in ["PERSON", "LOCATION", "ORGANIZATION"]:
            print(tag)

结果

('Bitcoin', 'LOCATION')       # wrong
('Francois', 'PERSON')
('R.', 'PERSON')
('Velde', 'PERSON')
('Federal', 'ORGANIZATION')
('Reserve', 'ORGANIZATION')
('Chicago', 'LOCATION')
('Richard', 'PERSON')
('Branson', 'PERSON')
('Virgin', 'PERSON')         # Wrong
('Galactic', 'PERSON')       # Wrong
('Bitcoin', 'PERSON')        # Wrong
('Bitcoin', 'LOCATION')      # Wrong
('Bitcoin', 'LOCATION')      # Wrong
('Paul', 'PERSON')
('Krugman', 'PERSON')
('Larry', 'PERSON')
('Summers', 'PERSON')
('Bitcoin', 'PERSON')        # Wrong
('Nick', 'PERSON')
('Colas', 'PERSON')
('ConvergEx', 'ORGANIZATION')
('Group', 'ORGANIZATION')     
('Bitcoin', 'LOCATION')       # Wrong
('BTC', 'ORGANIZATION')       # Wrong

Answer 5

您可以尝试对找到的名称进行解析，并检查是否可以在诸如 freebase.com 之类的数据库中找到它们。在本地获取数据并查询（在RDF中），或者使用google的api：https://developers.google.com/freebase/v1/getting-started。大多数大公司、地理位置等（会被你的 sn-p 捕获）可以根据 freebase 数据被丢弃。

Answer 6

这对我来说效果很好。我只需要更改一行就可以运行。

    for subtree in sentt.subtrees(filter=lambda t: t.node == 'PERSON'):

需要

    for subtree in sentt.subtrees(filter=lambda t: t.label() == 'PERSON'):

输出中存在缺陷（例如，它将“洗钱”识别为一个人），但根据我的数据，名称数据库可能不可靠。

Answer 7

我想在这里发布一个残酷而贪婪的解决方案，以解决@Enthusiast提出的问题：如果可能，请获取一个人的全名。

每个名称中第一个字符的大写用作识别Spacy 中的PERSON 的标准。例如，“吉姆霍夫曼”本身不会被识别为命名实体，而“吉姆霍夫曼”将被识别。

因此，如果我们的任务只是从脚本中挑选人物，我们可以简单地先将每个单词的第一个字母大写，然后将其转储到spacy。

import spacy

def capitalizeWords(text):

  newText = ''

  for sentence in text.split('.'):
    newSentence = ''
    for word in sentence.split():
      newSentence += word+' '
    newText += newSentence+'\n'

  return newText

nlp = spacy.load('en_core_web_md')

doc = nlp(capitalizeWords(rawText))

#......

请注意，这种方法以增加误报为代价覆盖全名。