1. NLP in 10 lines of code
Andraž Hribernik

2. AGENDA
1. NLP analysis of Pride & Prejudice
○ Introduction to spaCy API
○ Extract characters and visualize them relative to their position in the book
○ Extract adjectives that describes a character in the book
2. How we use spaCy at Cytora

3. Pride & Prejudice by Jane Austen
What is the book about?
○ 5 unmarried Bennet daughters
○ 2 young, wealthy gentlemen (Mr Bingley & Mr
Darcy) move into their neighbourhood
○ The oldest Bennet daughters (Jane & Elizabeth)
become involved with said gentlemen

5. Recreate the plot in 10 lines of code!
1. Parse text
2. Extract named entities
3. Keep only personal named entities
4. Get offset for every extracted entity
5. Plot the graph

6. 1. Parse text
import spacy
nlp = spacy.load('en')
text = open('pride_and_prejudice.txt').read()
processed_text = nlp(text)

7. 2. Extract named entities
import spacy
nlp = spacy.load('en')
text = open('pride_and_prejudice.txt').read()
processed_text = nlp(text)
for ent in processed_text.ents[:7]:
print(ent.text, ent.label_)
Output:
The Project Gutenberg EBook of ORG
Jane Austen PERSON
the Project Gutenberg License ORG
www.gutenberg.org FAC
Pride ORG
Jane Austen PERSON
August 26, 2008 DATE

8. 3. Keep only personal named entities
import spacy
nlp = spacy.load('en')
text = open('pride_and_prejudice.txt').read()
processed_text = nlp(text)
for ent in processed_text.ents[300:310]:
if ent.label_ == 'PERSON':
print(ent.text, ent.label_)
Output:
Bingley PERSON
Elizabeth PERSON
Darcy PERSON
William Lucas PERSON
Darcy PERSON

9. 4. Get offset for every extracted entity
...
processed_text = nlp(text)
character_offsets = defaultdict(list)
for ent in processed_text.ents:
if ent.label_ == 'PERSON':
character_offsets[ent.text].append(ent.start)
print(character_offsets['Elizabeth'][:5])
print(character_offsets['Darcy'][:5])
print(processed_text[1422])
print(processed_text[3229])
Output:
[1422, 3670, 3759, 3867, 4532]
[3005, 3229, 3367, 3410, 3754]
Elizabeth
Darcy

10. 5. Plot the graph
from collections import defaultdict
import spacy
nlp = spacy.load('en')
text = open('pride_and_prejudice.txt').read()
processed_text = nlp(text)
character_offsets = defaultdict(list)
for ent in processed_text.ents:
if ent.label_ == 'PERSON':
character_offsets[ent.lemma_].append(ent.start)
plot_character_timeseries(character_offsets, ['darcy', 'bingley'])

11. Demo

12. Describe Mr Darcy

13. Describe Mr Darcy
● Automatically describe Mr Darcy (e.g. silent, tall, young, etc)
● We can solve this problem using syntactic dependencies that are part of
spaCy API
● Syntactic dependencies could be very nicely visualized with displaCy

14. Describe Mr Darcy
adjective modifier

15. Extract all ‘amod’ dependencies in entities subtree
darcy_adjectives = []
darcy_ents = [ent for ent in processed_text.ents if
ent.lemma_ == 'darcy']
for ent in darcy_ents:
for token in ent.subtree:
if token.dep_ == 'amod':
darcy_adjectives.append(token.lemma_)
print(set(darcy_adjectives))
Output:
{'handsome', 'last', 'grave', 'silent',
'particular', 'young', 'poor',
'abominable', 'disappointing',
'disagreeable', 'confidential', 'late',
'little', 'charming', 'present',
'intimate'}

16. Describe Mr Darcy
adjective complement
noun subject

17. Extract all ‘acomp’ from entity’s root subtree
for ent in darcy_ents:
if ent.root.dep_ == 'nsubj':
for child in ent.root.head.children:
if child.dep_ == 'acomp':
darcy_adjectives.append(child.lemma_)
Output:
{'kind', 'ashamed', 'impatient',
'answerable', 'sorry', 'unworthy',
'grow', 'fond', 'proud', 'engaged',
'little', 'clever', 'worth', 'tall',
'studious', 'punctual'}

18. Pros & Cons of syntactic dependencies approach
● Training dataset is not needed
● Intuitive
● From our experiences, you can
achieve decent extraction
precision
● Our approach achieved very
poor recall
● Spacy dependency parsing
always works inside a single
sentence only

19. What is our mission at Cytora?

20. spaCy at Cytora
● We process 2M documents everyday with spaCy
● Named entity recognition (geolocations, actors)
● Dependency parsing (impact metric extraction)
● Integrated Word Embeddings (preprocessing for DL models)

21. Cytora is hiring!
● Data Engineer
● Data Science Analyst
● Risk Modeler
All open positions

22. Thank you!
https://github.com/cytora/pycon-nlp-in-10-lines
https://spacy.io/
https://demos.explosion.ai/displacy/
http://www.cytora.com/
andraz@cytora.com