I am trying to extract human names from text.
Does anyone have a method that they would recommend?
This is what I tried (code is below):
I am using nltk to find everything marked as a person and then generating a list of all the NNP parts of that person. I am skipping persons where there is only one NNP which avoids grabbing a lone surname.
I am getting decent results but was wondering if there are better ways to go about solving this problem.
Code:
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
Output:
LAST, FIRST
Velde, Francois
Branson, Richard
Galactic, Virgin
Krugman, Paul
Summers, Larry
Colas, Nick
Apart from Virgin Galactic, this is all valid output. Of course, knowing that Virgin Galactic isn't a human name in the context of this article is the hard (maybe impossible) part.
Must agree with the suggestion that "make my code better" isn't well suited for this site, but I can give you some way where you can try to dig in.
Disclaimer: This answer is ~7 years old. Definitely, it needs to be updated to newer Python and NLTK versions. Please, try to do it yourself, and if it works, share your know-how with us.
Take a look at Stanford Named Entity Recognizer (NER). Its binding has been included in NLTK v 2.0, but you must download some core files. Here is script which can do all of that for you.
I wrote this script:
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
and got not so bad output:
('Francois', 'PERSON') ('R.', 'PERSON') ('Velde', 'PERSON') ('Richard', 'PERSON') ('Branson', 'PERSON') ('Virgin', 'PERSON') ('Galactic', 'PERSON') ('Bitcoin', 'PERSON') ('Bitcoin', 'PERSON') ('Paul', 'PERSON') ('Krugman', 'PERSON') ('Larry', 'PERSON') ('Summers', 'PERSON') ('Bitcoin', 'PERSON') ('Nick', 'PERSON') ('Colas', 'PERSON')
Hope this is helpful.