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The	Use	of	NLP	to	
Solve	Problems
Annie	Flippo
11/2/2016
Who	am	I?
Annie	Flippo
Sr.	Data	Scientist
AwesomenessTV	/	Dreamworks	Animation	SKG
Slides	at	bit.ly/acflippo-nlp
Who	is	AwesomenessTV?
We’re	digital	content	provider	for	
platforms	including	Hulu,	Netflix,	Roku,	
Verizon	&	YouTube.
Business	Problem
Many	systems	managing	videos	
on	different	platforms
Goals
Develop	a	method	to	identify	same	
or	similar	assets	across	systems:
• Show	asset	relationship
• Generate	unique	id	for	in-house	apps
Why	use	NLP?
Top	goals	for	Natural	Language	
Processing	are:
1. Document	Similarity	(search	engine	query)
2. Topic	Modeling	(Twitter/Blog	Analysis)
3. Sentiment	Analysis	(movie	or	restaurant	reviews)
Data	Processing
Why	perform	text	processing?
• To	rid	of	messiness	of	free-from	text
• To	group	words	with	the	same	
meaning
• Convert	text	to	numeric	features
• Model	on	equivalent	numeric	features
Data	Processing
Titles	and	descriptions	get	scrubbed
• Remove	punctuation,	non-ascii,	carriage	
returns
• Remove	stop	words	(i.e.	it,	this,	and,	that)
• Stemming
• Lemmatize
• Tokenize
• Vectorize
Stemming
Reduce	to	the	root	of	the	word
Provision,	providing,	provider,	provided		
=>	provid
Argue,	argues,	arguing,	argued	=>	argu
Lemmatize
Retrieve	the	linguistic	root	of	the	word
Walk,	walking,	walked	=>	walk
Is,	am,	are	=>	be
Begin,	began,	begun	=>	begin
*Nouns	and	verbs	are	lemmatized	differently.
Tokenize
Count	distinct	words from	a	corpus
“The	quick	brown	fox	jumped	over	the	lazy	dogs”
becomes
[‘the’,	‘quick’,	‘brown’,	‘fox’,	‘jump’,	‘over’,	‘lazy’, ‘dog’]
Vectorize
Count	occurrences	from	distinct	word	
vector.
“The	quick	brown	fox	jumped over	the	lazy	dogs”
Tokenized	to:
[‘the’,	‘quick’,	‘brown’,	‘fox’,	‘jump’,	‘over’,	‘lazy’,	‘dog’]
Vectorized	to:
[2,	1,	1,	1,	1,	1,	1,	1]
Bag-of-Words	Comparison
Doc	1: “The	quick	brown	fox	jumped	over	the	lazy	dogs”
Doc	2:	“The	quick	fox	ran	away	from	the	dog”
After	processing,	the	corpus	attribute	vector	is:
[‘quick’,	‘brown’,	‘fox’,	‘jump’,	‘over’,	‘lazy’,	
‘dog’,	‘run’,	‘away’, ‘from’]
Two	documents	vectorize to:
Doc	1:	[1,	1,	1,	1,	1,	1,	1,	0,	0,	0]
Doc	2:	[1,	0,	1,	0,	0,	0,	1,	1,	1,	1]
Sentences	are	transformed	into	numeric	vectors!
brown
fox
lazy
run
quick
dog
Similarity	Measure
Cosine	similarity	calculates	how	close	2	numeric	vectors	
are	which	is	like	the distance	measure	between	2	
points.	
This	problem	has	just	reduced	to	simple	matrix	algebra.
Bi-Gram	Comparison
Due	to	the	same	words	used	across	our	videos,	the	bag-of-words	
similarity	resulted	high	false	positive	matches.		
The	solution	is	to	use	a	Bi-Gram	algorithm	where	2	consecutive	
words	are	extracted	as	one	feature:
“The	quick	brown	fox	jumped	over	the	lazy	dogs”
becomes:
[‘the	quick’,	‘quick	brown’,	‘brown	fox’,	‘fox	jump’,	
‘jump	over’,	‘over	the’,	‘the	lazy’,	‘lazy	dog’]
Limitations
Certain	phrases	such	as	“Behind	the	scenes”	are	
found	frequently.		This	creates	an	artificially	high	
similarity	score	even	if	the	videos	are	dissimilar.
Possible	solutions:
• Perform	more	custom	data	scrubbing
• Double-check	by	matching	duration	of	videos	
• Have	the	matches	verified	by	a	human
Conclusion
I	use	Natural	Language	Processing	to:
1. Identify	similar	videos	across	platforms
2. Tie	assets	together	where	some	are	identical	videos	
while	others	are	derived	videos	(such	as	trailers	or	
promos).
Thank	You!
Annie	Flippo @ACflippo
Slides	and	code	are	available	at
bit.ly/acflippo-nlp

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Use NLP to Solve Business Problems