Named Entity Recognition

Introduction to Natural Language Processing in Python

Katharine Jarmul

Founder, kjamistan

What is Named Entity Recognition?

NLP task to identify important named entities in the text
- People, places, organizations
- Dates, states, works of art
- ... and other categories!
Can be used alongside topic identification
- ... or on its own!
Who? What? When? Where?

Example of NER

NER on wikipedia article

(Source: Europeana Newspapers (http://www.europeana-newspapers.eu))

nltk and the Stanford CoreNLP Library

The Stanford CoreNLP library:
- Integrated into Python via nltk
- Java based
- Support for NER as well as coreference and dependency trees

Using nltk for Named Entity Recognition

import nltk
sentence = '''In New York, I like to ride the Metro to 
              visit MOMA and some restaurants rated 
              well by Ruth Reichl.'''
tokenized_sent = nltk.word_tokenize(sentence)

tagged_sent = nltk.pos_tag(tokenized_sent)

tagged_sent[:3]

[('In', 'IN'), ('New', 'NNP'), ('York', 'NNP')]

print(nltk.ne_chunk(tagged_sent))

(S
  In/IN
  (GPE New/NNP York/NNP) 
  ,/,
  I/PRP
  like/VBP
  to/TO
  ride/VB
  the/DT
  (ORGANIZATION Metro/NNP)
  to/TO
  visit/VB
  (ORGANIZATION MOMA/NNP)
  and/CC
  some/DT
  restaurants/NNS
  rated/VBN
  well/RB
  by/IN
  (PERSON Ruth/NNP Reichl/NNP) 
  ./.)

Let's practice!

Introduction to Natural Language Processing in Python