Using NLP to Accelerate Product Development and Help You Create a Killer App

Using NLP to Accelerate
Product Development and
Help You Create a Killer App
Max Kaufmann, November 2015

© 2014 International Business Machines Corporation 2
Our Speaker:
Max Kaufmann
NLP Developer
IBM Watson Ecosystem
Max Kaufmann has a BS in Linguistics from Grinnell College, and a M.S. in Computational
Linguistics from the University of Washington. He has previous experience in academia, where
he has published papers on topics such as using machine translation, and in industry, where he
has several NLP-related patents pending. He is currently a member of the IBM Watson
Ecosystem team, where he helps help fast growing companies and entrepreneurial minded
organizations use Watson's NLP capabilities to solve real world problems.

Agenda:
• What is NLP and why do you need it?
• What are the main problems in NLP?
• How are NLP problems solved?
• Why the Watson Ecosystem is awesome
• Q & A

-Wikipedia
“Teaching computers to understand human languages”
− Max Kaufmann
What is NLP?

Why NLP is hard
• Ambiguity
– I saw her duck with a telescope

Why NLP is hard
• Novelty
– Watson is super duper kewl, I'm so excited to be an
Ecosystem Partner #watson #ibm
#subliminalMessaging
– Omg rmbr that sandwhich I ate for lunch it was
soooo dope #sandwhich #food
#everyoneNeedsToKnowWhatIateforLunch
• Lots of NLP systems are ‘trained’ on text from
newspapers/books.

Required Knowledge: Who
Ginni is, what 'behind'
means
Possible answers: 'a sign'
'the ibm logo' 'multiple signs'
Why NLP is hard
• World Knowledge
– What’s behind Ginni?

s: http://acl2014.org/
Why should I care about NLP?
• These companies do:

Sources: http://altaplana.com/TextAnalytics2014.pdf
What applications involve NLP

What type of data requires NLP?

What capabilities can NLP add?

What types of problems are there in
NLP?

Type 1: Applications
• These are things you all want to do
– Natural Language Generation
– Summarization
– Dialog
– Machine Translation
– Question Answering
– Sentiment Analysis

Type 2: Tasks
• These are usually precursors to applications
– Part of Speech tagging
• Identify which part of speech a word is
– Lemmatizing
• Running  run, ran  run
– Multiword Expression (Idiom) Identification
• ‘Kick the bucket’ can’t become ‘kicking the bucket’
– Parsing
– Word sense disambiguation
• ‘bank’ as in money vs ‘bank’ as in river
– Conference/Anaphora Resolution
• Sally didn’t know what to do with all the money she made by
becoming a Watson Ecosystem partner

If I want to tackle NLP on my own,
what would a solution look like?

How to Solve NLP Problems
• Sample problem: Relationship Extraction
• Input:
– Today I saw Ginni Rometty give an amazing talk about
Watson. She was a fantastic speaker, I want her to give a talk
at my organization.
• Goal: Extract all relations involving people.
• Solution: Chain a bunch of tasks together until you have
enough information to extract relations
– Going to focus on the pipeline process, not implementation

Sentence Splitting
• Input:
Watson. She was a fantastic speaker, I want her to give a talk
at my organization.
• Output:
Watson.
– She was such a fantastic speaker, I want her to give a talk at
my organization.
• Food for thought:
– How would we split if it said Mrs. Rometty?

Pop quiz: How many POS tags are there?
− 36
 Today I saw Ginni Rometty give an amazing talk about Watson.
− Today = Adverb
 Today is a great day
− I = Pronoun
− Saw = Verb
 Why not the cutting tool?
− Ginni = Proper noun
Part of Speech (POS) tagging

Key
N = Noun
NNP = Proper Noun
PRP = Personal Pronoun
DT = Determiner
JJ = Adjective
IN = Preposition
NP, VP, ect = Noun Phrase,
Verb Phrase,ect
NX,VX,ect = Incomplete NP,
Incomplete VP
Parsing

Why is Ginni a person,
but Watson isn't?
Entity Identification

Relationship Extraction
• Today I saw Ginni Rometty give an amazing talk about
Watson. She was such a fantastic speaker, I want her to
give a talk at my organization.
• Which is right?
– Give (an amazing talk about Watson, Ginni Rometty)
– Give (an amazing talk, Ginni Rometty)
– Give (talk, Ginni Rometty)
• Extracting speaker (Ginni Rometty)
– How do we know that (she == Ginni Rometty) but (I ! = Ginni
Rometty)

Relationship Extraction
• How do we express that the talk hasn’t happened yet?
– What if the sentence was “I want her to give another talk at my
organization”
• “She was a fantastic speaker”
– Was it this time only? Or is this a property of Ginni?

What did we learn?
• NLP is hard
– Language will always surprise you.
• Everything is a pipeline
– Part of Speech tagging  Parsing  Entity Detection 
Relationship extraction
– What if we had identified “Ginni Rometty” as a verb?

How do you build applications that
deal with language?
• See what progress has been made on your problem
– “basically solved”
– “good progress”
– “here be dragons”

What problems have we basically solved?
• Part of Speech tagging
– I went to the store  Store
• Lemmatizing
– Running  run
• Morphological segmentation
– Running  run + ing
• Sentence Splitting
• Tokenizing
– Breaking sentences into ‘words’

What problems have we made good progress on?
• Machine Translation
• Parsing
• Search
• Coreference Resolution
• Sentiment Analysis
• Relationship Extraction
• Word Sense Disambiguation
• Idiom Identification

http://www.datacommunitydc.o
rg/blog/2013/05/recommendati
on-engines-why-you-shouldnt-
build-one
‘Here be Dragons’ Problems
• Summarization
• Natural Language Generation
• Dialog
• Content Recommendation
• Artificial Intelligence
• Question Answering
• Building Ontologies
• Pragmatics

More Reasons Why NLP is hard
• Accept that things will go wrong
– Nothing in NLP ever has 100% accuracy
• Accept that NLP numbers are uncomfortable
– 50% accuracy can be very good
– Going from 72%  74% accuracy can be a HUGE deal
• Embrace Cognitive Computing
– “While they’ll have deep domain expertise, instead of replacing
human experts, cognitive computers will act as a decision
support system and help them make better decisions based
on the best available data, whether in healthcare, finance or
customer service.”

It's difficult to do NLP if it's not your core business
competency
Are there any companies that can help?

We love feedback!
Why our APIs are awesome

Lets you figure out if it works for you
Easy REST API
Deploys in
Bluemix

Science

Summary
• NLP is hard to tackle on your own
• But if your application involves users, NLP can provide
huge value
• The best way to get that value without all the hard work is
to become an Ecosystem Partner

What now?
• Explore the Watson APIs:
http://www.ibm.com/smarterplanet/us/en/ibmwatson/devel
opercloud/services-catalog.html
• Apply to become an Ecosystem Partner:
http://www.ibm.com/smarterplanet/us/en/ibmwatson/ecos
ystem.html

Questions?

Using NLP to Accelerate Product Development and Help You Create a Killer App

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