NLP | Extracting Named Entities

Last Updated : 26 Feb, 2019

Recognizing named entity is a specific kind of chunk extraction that uses entity tags along with chunk tags.
Common entity tags include PERSON, LOCATION and ORGANIZATION. POS tagged sentences are parsed into chunk trees with normal chunking but the trees labels can be entity tags in place of chunk phrase tags. NLTK has already a pre-trained named entity chunker which can be used using ne_chunk() method in the nltk.chunk module. This method chunks a single sentence into a Tree.

Code #1 : Using ne-chunk() on tagged sentence of the treebank_chunk corpus

from nltk.corpus import treebank_chunk 
from nltk.chunk import ne_chunk 
  
ne_chunk(treebank_chunk.tagged_sents()[0]) 

Output :

Tree('S', [Tree('PERSON', [('Pierre', 'NNP')]), Tree('ORGANIZATION', 
[('Vinken', 'NNP')]), (', ', ', '), ('61', 'CD'), ('years', 'NNS'), 
('old', 'JJ'), (', ', ', '), ('will', 'MD'), ('join', 'VB'), ('the', 'DT'),
('board', 'NN'), ('as', 'IN'), ('a', 'DT'), ('nonexecutive', 'JJ'), 
('director', 'NN'), ('Nov.', 'NNP'), ('29', 'CD'), ('.', '.')])

two entity tags are found: PERSON and ORGANIZATION. Each of these subtrees contains a list of the words that are recognized as a PERSON or ORGANIZATION.

Code #2 : Method to extract named entites using leaves of all the subtrees

def sub_leaves(tree, label): 
    return [t.leaves()  
            for t in tree.subtrees( 
                    lambda s: label() == label)] 

Code #3 : using method to get all the PERSON or ORGANIZATION leaves from a tree

tree = ne_chunk(treebank_chunk.tagged_sents()[0]) 
  
from chunkers import sub_leaves 
print ("Named entities of PERSON : ",  
       sub_leaves(tree, 'PERSON')) 
  
print ("\nNamed entites of ORGANIZATION : ",  
       sub_leaves(tree, 'ORGANIZATION')) 

Output :

Named entities of PERSON : [[('Pierre', 'NNP')]]

Named entites of ORGANIZATION : [[('Vinken', 'NNP')]]

To process multiple sentences at a time, chunk_ne_sents() is used. In the code below, first 10 sentences from treebank_chunk.tagged_sents() are processed to get ORGANIZATION sub_leaves().

Code #4 : Let’s understand chunk_ne_sents()

from nltk.chunk import chunk_ne_sents 
from nltk.corpus import treebank_chunk 
  
trees = chunk_ne_sents(treebank_chunk.tagged_sents()[:10]) 
[sub_leaves(t, 'ORGANIZATION') for t in trees] 

Output :

[[[('Vinken', 'NNP')]], [[('Elsevier', 'NNP')]], [[('Consolidated', 'NNP'), 
('Gold', 'NNP'), ('Fields', 'NNP')]], [], [], [[('Inc.', 'NNP')], 
[('Micronite', 'NN')]], [[('New', 'NNP'), ('England', 'NNP'),
('Journal', 'NNP')]], [[('Lorillard', 'NNP')]], [], []]

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