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M-CAFE	Topic	Tagging	
With	Watson
Dataset
§ M-CAFE	for	IEOR	115:	16	
weeks	in Aug - Dec,	2015
• Student	count:	115
• Idea	count:	106
§ 106	ideas	with	tags	are	
split	randomly	into	train	
(86	ideas)	and	test	(20	
ideas).
Watson NaturalLanguageClassifier
Topic Tagging with Watson by Ken Goldberg, UC Berkeley
Train&Test Sets
• Train:	86 ideas with topics	tagged.
• Test:	20	ideas	without	topics	tagged.
Screen	capture	of	the	.csv	file	for	training	set
Code
• curl	-i -u	"896090f0-631f-4745-b02a-
47b6417140d6":"xuDyj6lD9USr"	-F	
training_data=@/Users/apple/Desktop/mcafe_watson_train.c
sv -F	
training_metadata="{"language":"en","name":"McafeCl
assifier"}"	"https://gateway.watsonplatform.net/natural-
language-classifier/api/v1/classifiers"	
• curl	-G	-u	"896090f0-631f-4745-b02a-
47b6417140d6":"xuDyj6lD9USr"	
"https://gateway.watsonplatform.net/natural-language-
classifier/api/v1/classifiers/3AE103x13-nlc-1276/classify"	--
data-urlencode"text=testData"
Test	Result:	80%	Accuracy!
Out	of	the	20	test	samples,	16	
were	corrected	classified.
Idea Topic
Slower	pace. Lectures
Add	Lecture	overview Resources
I	want	more	practice	with	Relational	Algebra	and	eventually	SQL. Homework
The	last	few	lectures	have	been	very	mathematically	precise	in	
notation	which	can	make	it	a	bit	tricky	to	wrap	your	head	around.	
Specific	questions/examples	(like	what	might	be	on	hw)	would	be	
great	to	help	us	make	sure	we	understand	it	moving	forward.
Lectures
The	project	seems	a	little	stop	and	go.	We	haven't	been	able	to	
work	on	it	for	a	week	or	so	but	I	feel	like	we'll	soon	be	expected	
to	do	a	bunch	of	work	for	DP2.	It	would	be	helpful	if	we	could	
have	the	tools	to	have	a	more	constant	level	of	work	on	the	
project.
Projects
Please	try	and	post	the	labs	earlier	so	that	we	can	get	a	head	
start	reading	and	understanding	them.
Labs
Homework	2	only	has	database	questions,		maybe	put	some	
connectives?
Homework
Incorporate	a	short	question	and	answer	period	midway	of	
lecture	to	assess	participating	students'	understanding	of	the	
lecture/topics	being	presented.
Lectures
Examples	of	ideas	which	are	correctly	classified:
Misclassifications
• The	true	tag	is	among	the	top	two	tags	suggested	by	the	
classifier.
• Misclassification	occurs	when	an	idea	is	arbitrarily	tagged	
or	with	lack	of	context.
Idea True	Tag Pred Tag Confidence
1.	slow	down	a	little	bit Lectures Resources
Resources:	0.288;	
Lectures:0.224
2.	It	would	be	great	if	
you	could	provide	
outside	resources	on	
rules	and	guidelines	for	
things	like	ER	diagrams	
that	you	think	are	worth	
our	time.	
Resources Lectures
Lectures:	0.879;	
Resources:0.130
Idea True	Tag Pred Tag Confidence
3.	I	would	like	have	some	
implantation	problems	
using	SQL
Homework New	Topics
New	Topics:	
0.803;	
Homework:	
0.076
4.	More	hands	on	
experiences	on	Databases
Homework New	Topics
New	Topics:	
0.786;	
Homework:	
0.117
Misclassifications	Contd…
• The	true	tag	is	among	the	top	two	tags	suggested	by	the	
classifier.
• Misclassification	occurs	when	an	idea	is	arbitrarily	tagged	
or	with	lack	of	context.
Questions	for	IBM
• 1.	How	is	the	classifier	trained?		What	is	the	
classification	method?
• 2.	Is	there	a	version	of	the	classifier	that	can	return	
the	predicted	topic	for	the	test	set?	
• 3.	This	essentially	a	supervised	classification	
problem,	does Watson	have	an	unsupervised	
version	available,	just	provide	raw	text	and	it	
would	assign	tags?

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Topic Tagging with Watson by Ken Goldberg, UC Berkeley