Big Data and Natural Language Processing

www.decideo.fr/bruley
Natural Language ProcessingNatural Language Processing
June 2013
Michel Bruley

Natural Language Processing (NLP)Natural Language Processing (NLP)
NLP is the branch of computer science focused on developing systems
that allow computers to communicate with people using everyday
language
NLP is considered as a sub-field of artificial intelligence and has
significant overlap with the field of computational linguistics. It is
concerned with the interactions between computers and human (natural)
languages.
– Natural language generation systems convert information from
computer databases into readable human language
– Natural language understanding systems convert human language
into representations that are easier for computer programs to
manipulate.
NLP encompasses both text and speech, but work on speech processing
has evolved into a separate field

Where does it fit in the CS*Where does it fit in the CS*
taxonomy?taxonomy?
Computers
Artificial Intelligence AlgorithmsDatabases Networking
Robotics SearchNatural Language Processing
Information
Retrieval
Machine
Translation
Language
Analysis
Semantics Parsing* CS = Computer Science

Why Natural Language Processing?Why Natural Language Processing?
Applications for processing large amounts of texts require NLP expertise
Classify text into categories, index and search large texts: Classify documents
by topics, language, author, spam filtering, information retrieval (relevant, not
relevant), sentiment classification (positive, negative)
Extracting data from text: converting unstructured text into structure data
Information extraction: discover names of people and events they participate in,
from a document, …
Automatic summarization: Condense 1 book into 1 page, …
Speech processing, artificial voice: get flight information or book a hotel over
the phone, …
Question answering: find answers to natural language questions in a text
collection or database
Spelling & Grammar Corrections
Plagiarism detection
Automatic translation
Etc.

The problemThe problem
When people see text, they understand its meaning (by and large)
According to research, it deosn’t mttaer in what oredr the ltteers in a
wrod are, the olny iprmoetnt tihng is that the frist and lsat ltteer are in
the rghit pclae. The rset can be a toatl mses and you can sitll raed it
wouthit a porbelm. Tihs is bcuseae we do not raed ervey lteter by islelf
but the wrod as a wlohe.
When computers see text, they get only character strings (and perhaps
HTML tags)
We'd like computer agents to see meanings and be able to intelligently
process text
These desires have led to many proposals for structured, semantically
marked up formats
But often human beings still resolutely make use of text in human
languages
This problem isn’t likely to just go away

Example: Natural languageExample: Natural language
understandingunderstanding
Raw speech signal
• Speech recognition
Sequence of words spoken
• Syntactic analysis using knowledge of the grammar
Structure of the sentence
• Semantic analysis using info. about meaning of words
Partial representation of meaning of sentence
• Pragmatic analysis using info. about context
Final representation of meaning of sentence
Natural language understanding process – Prof. Carolina Ruiz

Example detail: Syntactic AnalysisExample detail: Syntactic Analysis
The big cat is drinking milk
Noun Phrase Verb Phrase
Determiner Adjective
Phrase
Noun Auxiliary Verb Noun
Phrase
The big cat is drinking milk
• Syntactic analysis involves isolating phrases and sentences into a
hierarchical structure, allowing the study of its constituents.
• For example the sentence “the big cat is drinking milk” can be broken
up into the following constituents:

Why NLP is difficultWhy NLP is difficult
Language is flexible
– New words, new meanings
– Different meanings in different contexts
Language is subtle
– He arrived at the lecture
– He chuckled at the lecture
– He chuckled his way through the lecture
– **He arrived his way through the lecture
Language is complex!

MANY hidden variables
– Knowledge about the world
– Knowledge about the context
– Knowledge about human communication techniques
• Can you tell me the time?
Problem of scale
– Many (infinite?) possible words, meanings, context
Problem of sparsity
– Very difficult to do statistical analysis, most things (words,
concepts) are never seen before
Long range correlations

Key problems:
– Representation of meaning
– Language presupposes knowledge about the world
– Language only reflects the surface of meaning
– Language presupposes communication between people

Patented Natural Language Processing (NLP)Patented Natural Language Processing (NLP)
“Reads” Every Communication“Reads” Every Communication
 Each data feed is parsed
through one or more of the 7
NLP engines
 …it is then deconstructed to
provide context, subject, and
other information regarding
the customer (gender, name
etc.)
 Finally each identified
customer is matched back to
the Discovery platform data
to gain a full view
Natural language processing (NLP) is the study of the
interactions between computers and natural languages
(e.g., English, Polish). The crucial challenge that NLP
addresses is in deriving meaning from human or natural
language input and allowing consumers to analyze
parsed meanings in large volumes.

For Example….For Example….
I bought an iPad2 for my mom last week. She loves the weight, but doesn’t like the color. She
wishes it came in blue. She says if it came in blue, then she’d buy one for all her friends
Entities (brands, people, locations, times, products…)
Events and relationships (purchasing event, my mom…)
Sentiment (product specifications)
Suggestions (feature specifications)
Intent (to purchase, to leave)
Geo/Temporal
QUESTION: Why is this a big deal?
NLP takes a simple English statement, parses them into the categories above (and more categories)
and VOILA…we got STRUCTURED DATA

Aster
ASTER DISCOVERY
PLATFORM
“Now-
structured”
data
“Now-
structured”
data
ArchitectureArchitecture
Customers /
Sales / Other
data
Customers /
Sales / Other
data
Churn Score
SQL MR
Churn Score
SQL MR
Attensity Pipeline
Real-time
annotated
social media
data feed:
150+ million
social and
online sources
Other Unstructured Data
Emails; Surveys;
CRM Notes….
Pipeline Connector
ASAS
Wrapper
SQL MR
ASAS
Wrapper
SQL MR
NLP
ETL
Visualization
(e.g., Tableau,
MSTR)
Predictive

 This integration provides types, subtypes, super types (“Savings”, “Checking”,
“Investment”)
 Inclusion of the Anaphora: Connecting a subject (George Harrison) without
repeating the full name (“He”, “Him”)
 Includes other languages besides English
 Attensity’s Semantic Annotation Server (ASAS) capabilities
 Entity Extraction: Automatic detection and extraction of more than 35 entities such as Name,
Place
 Uses Attensity Triples to create context on entities and identify verbs, relationships, actions
 Auto Classification: Uses custom classification rules to classify articles by content, sort by
relevance, and discovers repeated information
 Exhaustive Extraction: Application of linguistic principles to extract context, entities, and
relationships similar to how the human mind would
 Voice Tags: to identify types of statements and auto classify them (Question, Intent,
Conditional)
 Creates a unique identifier for each entity for cross reference
Aster + Attensity = CompetitiveAster + Attensity = Competitive
AdvantageAdvantage

Structuring Unstructured Data: ProcessStructuring Unstructured Data: Process
FlowFlow
The flight was delayed and flight attendant would not give us
any new information.

New Table: Customer Reactions
Database Record from a Customer Survey
date
10-02-06
region
0006
rec?
4
source
telephone
Why would you recommend/not recommend?
The flight was delayed and flight attendant would
not give us any new information.
Who/What
flight
Behavior
delay
Fact/Triple
flight : delay
Same Record with Relational Facts
Extracted from Notes Field
date region source rec? who-what Behavior Fact/Triple
10-2-12 0006 telephone 4 flight delay flight : delay
10-2-12 0006 telephone 4 information give [not]
information :
give [not]
1-1-13 0007 e-mail 8 i happy [not] i : happy [not]
1-1-13 0007 e-mail 8 rep rude rep : rude
1-1-13 0007 e-mail 8 flight cancel flight : cancel
Original Structured Data
Newly Structured Data
Provided by Attensity
How Triples are Extracted &How Triples are Extracted &
StructuredStructured
Extract
Extract relational facts & Triples
from Notes field
Then Fuse
Populate new table with
attribute values and fuse with
structured data.

Team PowerTeam Power

Big Data and Natural Language Processing

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