Improve this question
我正在尝试从文本中提取人名。
有人有推荐的方法吗？
这是我尝试过的(下面的代码):
我正在使用nltk查找标记为人的所有东西，然后生成该人所有NNP部分的列表。我正在跳过只有一个NNP可以避免抓住一个姓氏的人。
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。
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，在本文中了解维珍银河不是人的名字是很困难的(也许是不可能的)部分。
参考方案
必须同意“使我的代码更好”这个网站不适合的建议，但是我可以给你一些方法，让你尝试挖掘。
看一看Stanford Named Entity Recognizer (NER)。它的绑定已包含在NLTK v 2.0中，但是您必须下载一些核心文件。这是script，可以为您完成所有操作。
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
并没有那么糟糕的输出:
(“Francois”，“PERSON”)
(“R。”，“PERSON”)
(“Velde”，“PERSON”)
(“Richard”，“PERSON”)
(“布兰森”，“人”)
(“处女”，“人”)
(“银河系”，“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”)
希望这会有所帮助。
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