Improving the extraction of human names with nltk

You can try to do resolution of the found names, and check if you can find them in a database such as freebase.com. Get the data locally and query it (it’s in RDF), or use google’s api: https://developers.google.com/freebase/v1/getting-started. Most big companies, geographical locations, etc. (that would be caught by your snippet) could be then discarded based on the freebase data.

For anyone else looking, I found this article to be useful: 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())
...

This worked pretty well for me. I just had to change one line in order for it to run.

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

needs to be

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

There were imperfections in the output (for example it identified “Money Laundering” as a person), but with my data a name database may not be dependable.

The answer of @trojane didn’t quite work for me, but helped a lot for this one.

Prerequesites

Create a folder stanford-ner and download the following two files to it:

• english.all.3class.distsim.crf.ser.gz
• stanford-ner.jar (Look for download and extract the archive)

Script

#!/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)

Results

('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

I actually wanted to extract only the person name, so, thought to check all the names that come as an output against wordnet( A large lexical database of English).
More Information on Wordnet can be found here: 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)

OUTPUT

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

Apart from Larry Summers all the names are correct and that is because of the last name “Summers”.

I would like to post a brutal and greedy solution here to solve the problem cast by @Enthusiast: get the full name of a person if possible.

The capitalization of the first character in each name is used as a criterion for recognizing PERSON in Spacy. For example, ‘jim hoffman’ itself won’t be recognized as a named entity, while ‘Jim Hoffman’ will be.

Therefore, if our task is simply picking out persons from a script, we may simply first capitalize the first letter of each word, and then dump it to 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))

#......

Note that this approach covers full names at the cost of the increasing of false positives.