Improving the extraction of human names with nltk [closed]

Question

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.

Answer 1

Must agree with 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.

Take a look at Stanford Named Entity Recognizer (NER). Its binding has been included into 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.

Answer 2

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()) ...