Natural language processing (NLP) - once on the frontier of AI as a research topic with maddeningly low accuracy - is rapidly becoming a requirement for mainstream consumer and enterprise applications. Today, one can build a system that allows natural language text or speech input without knowing much more than a few API specs. In this webinar we will cover the basics of speech recognition, semantic analysis for text analysis, and recent advances in natural language generation. Participants will learn how modern approaches have gone beyond counting words with statistical models to predicting speech the way people fill in sentences with context while listening. We will also present examples of commercially available NLP APIs to help participants experiment with NLP in their own applications.

1. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Advances in Natural Language Processing
Adrian Bowles, PhD
Founder, STORM Insights, Inc.
info@storminsights.com

2. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
Advances in Natural Language Processing
Context: NLU vs NLG
NLU
Technology
Market
Applications
NLG
Technology
Market
Applications
Next Steps

3. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
“You’re Not Just Responsible For What You Say,
You Are Responsible For What People Hear”
August 9, 2016
General Michael Hayden (Retired), former Director,
Central Intelligence Agency and National Security Agency
on advice to his senior staff.
In
the
N
ew
s

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X
X’
Y
Under ideal conditions, people are good - but not perfect - when
communicating in natural languages. We…
understand in context (environment & our own frame of reference)
attempt to resolve ambiguity
have to deal with competing signals, noise
fill in words and meaning and may not hear/understand - what was said/meant…

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In
the
N
ew
s

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Common Sense vs
Canned responses…

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In
the
N
ew
s

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In
the
Press

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Why is it so difficult?
NL Grammar Theories Abound
Generative Grammar - system of rules that specify the complete set of valid strings/sentences in a language.
regular grammars, context free grammars…
Constraint-based Grammar - system of rules that specify constraints on strings/sentences in a language.
Anything not constrained is valid.
Stochastic Grammar - “correctness” based on probability, similar to fuzzy set theory.
…
NLs are inherently ambiguous
Cultural differences
Sarcasm
Idioms
Metaphors

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In English, two negatives alway make a positive.
In English, two positives never make a negative.
That is not an uncommon occurrence. That is a common occurrence.
Yeah, right. Oops…sarcasm is hard.
Inflection matters, “literally” usually isn’t…
Rules…or guidelines?
Why is it so difficult?

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Generative Grammar - system of rules that specify the complete set of valid strings/sentences in a language.
regular grammars, context free grammars…
The Infinite Monkey Theorem
<postal-address> ::= <name-part> <street-address> <zip-part>
<name-part> ::= <personal-part> <last-name> <opt-suffix-part> <EOL>
| <personal-part> <name-part>
<personal-part> ::= <initial> "." | <first-name>
<street-address> ::= <house-num> <street-name> <opt-apt-num> <EOL>
<zip-part> ::= <town-name> "," <state-code> <ZIP-code> <EOL>
<opt-suffix-part> ::= "Sr." | "Jr." | <roman-numeral> | ""
<opt-apt-num> ::= <apt-num> | ""
Backus–Naur Form. (2016, June 27). In Wikipedia, The Free Encyclopedia. Retrieved 14:42, August 11, 2016,
from https://en.wikipedia.org/w/index.php?title=Backus%E2%80%93Naur_Form&oldid=727250296
Bob Smith 22 Main Street 06880
Why is it so difficult?

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Natural Language Processing
NLP
Natural Language Understanding
NLU
Natural Language Generation
NLG
?
Key Concept: What is Understanding?

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SemanticsText Syntax
Voice
Structure
Natural Language Understanding
NLU
Meaning
Modeled
Understanding

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Natural Language Processing
NLP
Natural Language Understanding
NLU
Natural Language Generation
NLG
Computational Linguistics - modeling natural language with rules or statistical models
Static performance based on preprogrammed logic/model
vs
Learning systems that improve performance based on feedback

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Natural Language Understanding
NLU
Natural Language Generation
NLG
Statistical
Modeling
Semantic
Analysis
Syntactic
Analysis
Analysis Synthesis
Models
&
Representations

16. Human
Sensors/
Systems
Input/NLU Output/NLG
Visualization
Narrative GenerationVoice/NLP
Video/Images
Reports
Gestures
Emotions
Text/NLP
Surface Structured Data
Surface Structured Data
Reports
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
NLP In Context
• Gridspace
• IBM
• Maluuba
• MindMeld
• Nuance
• PopupArchive
• Skymind
• Viv Labs
• Wit.ai
• ABBYY
• Altilia
• Cortical.io
• Digital Reasoning
• Google
• IBM
• LoopAI
• Luminoso
• Maluuba
• Wit.ai
• Arria
• Automated Insights
• Ax-Semantics
• Narrative Science
• Retresco
• Yseop
Perception/
NLP
Problem Solving
Simple:
deterministic,
retrieve/calculate
Complex: probabalistic
hypothesize, test, rank,
select
Creative:
discover, generate
ORGANIZED
Memory*

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Google: Smart Reply (email responder)

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Proximity Modeling
Boy
Bay
Map
Mop
Man
Nay May
Mope
Buy
Hop
Mapped with vectors,
proximity algorithm
based on purpose.
Mapping for autocorrect/complete vs Mapping for meaning
Boy
Bay
Map
Mop
Man
Nay
May
Mope
BuyHop
Hope
Hope
Similar structure ->
similar meaning in vision,
not always in language.
NLU Technology

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Google Cloud NLP
Focus: Extract meaning
NLU

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Copyright (c) Digital Reasoning.
NLU

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NLU
Copyright (c)
Digital Reasoning.

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NLU
Copyright (c)
Digital Reasoning.

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NLU
Copyright (c)
Digital Reasoning.

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DATA
Model/
Article
Structure
Template
Generated
Narrative
From simple chattiest*
to structured responses
to prose.
*I actually typed “chatbot” but
without “” the app “corrected” me.
Natural Language Generation
NLG

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NLG
In
the
N
ew
s

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NLG
In
the
N
ew
s

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DATA/
Model
Generated
Narrative
Vendors:
Automated Insights (Wordsmith)
Narrative Science (Quill)
Washington Post/Arc (Heliograf)
Arria (UK)
Ax-Semantics (Germany)
Retresco (Germany)
Yseop (France)
Natural Language Generation Market
NLG

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DATA
Ticket listings
Sale data
Customer history
Model/
Article
Structure
Template
Application: NLG emails for customer satisfaction
Developer: Orlando Magic
Product: Automated Insights Wordsmith
Time to Deploy: <1 week

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Sentiment/Emotion/Theme/Concept Analysis
The lingering question
What Is Understanding?
Don’t let the search for perfection
interfere with the path to progress.

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Next Steps
For both NLU and NLG
Commercial technologies today are imperfect but useful!
Do you want to derive data/insights from NL? (NLU)
Do you want to create content from data? (NLG)
Get Started Now

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Build it Yourself With…

34. Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.Source: IBM Bluemix June 9, 2016
Build it Yourself With…

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IBM Watson Conversation
Build it Yourself With…

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Build it Yourself With…

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Build it Yourself With…

38. For more information:
Copyright (c) 2016 by STORM Insights Inc. All Rights reserved.
adrian@storminsights.com
Twitter @ajbowles
Skype ajbowles
Upcoming Webinar Dates & Topics
September 8 Autonomous Systems, from Science Fiction to Commercial Solutions
October 13 Deep QA (Question/Answer) - Lessons From Watson and Jeopardy!
November 10 Emerging Hardware Choices for Modern AI Data Management
December 8 Leverage the IOT to Build a Smart Data Ecosystem