Why you need Deep Semantic Analytics MeaningCloud webinar

Achieve a Deep,
Automated Understanding
of Complex Documents
July 13, 2017
Webinar

Why you need Deep Semantic Analytics
Before we get started…
Presenter
How to participate
• Send questions with the chat feature, or
• Click the “Raise your hand” button to speak
and we’ll enable your mic
• Afterwards, you’ll be able to access a recording of the
webinar and its contents as tutorials on our blog
Antonio Matarranz
CMO

The purpose of this webinar…
Understand the value of
Deep Semantic Analytics
of complex documents

Agenda
 Automatic understanding of unstructured documents.
 What is Deep Semantic Analytics? Comparison with
conventional text analytics.
 Where it can be applied.
 Case study: due diligence process.
 Ideal features of a Deep Semantic Analytics solution.
 MeaningCloud Roadmap in Deep Semantic Analytics.
 Conclusions and questions.

Opinions
Facts
Concepts
Organizations
People
Semantic
Analysis
Relationships
Themes
Text analytics
A first level of automatic understanding of unstructured content

Extracting isolated information elements
 Detect in an unambiguous and and context-dependent way occurrences of
topics under various synonyms
 Theme categorization
 Much more than detecting text strings
Donald Trump takes major step to wiping out Obama’s climate-change
record
Presidential order will instruct federal regulators to rewrite key rules curbing
carbon emissions
President Trump’s executive order will tell the EPA to begin rewriting the 2015 regulation
that limits greenhouse-gas emissions from existing power plants Justin Lane/EPA
President Trump will take the most significant step yet in obliterating his predecessor’s
environmental record on Tuesday, instructing federal regulators to rewrite key rules
curbing US carbon emissions.
The sweeping executive order also seeks to lift a moratorium on federal coal leasing and
remove the requirement that federal officials consider the impact of climate change when
making decisions.
The order sends an unmistakable signal that just as President Barack Obama sought to
weave climate considerations into every aspect of the federal government, Trump is
hoping to rip that approach out by its roots.
Entities:
Barack H. Obama, POTUS44
Donald J. Trump, POTUS45
Concepts:
Greenhouse gas emisions
Climate change
Presidential order
Federal government
Federal regulator
Themes:
Global warming
US legislation

Extracting isolated data is not enough
What if I need to find…?
"All contracts where Mr. Holden Caulfield
appears as buyer of a real estate worth
more than $250,000."
"All companies that have a shareholder
that owns more than 40% of the stock."
"All news that talk about the possible
acquisition of a company by Google."

Conventional Text Analytics
Discover the deep meaning of
complex documents
We need to go beyond conventional Text Analytics

Deep Semantic Analytics / Text Analytics comparison
Conventional Text Analytics Deep Semantic Analytics
 Information extraction: named entities,,
concepts, quantities…
 General theme categorization
 Sentiment analysis
 Clustering
 Automatic summarization
 Etc.
What conventional Text Analytics
offers +
 Passage-level categorization
 Extraction of semantic relationships
 Other functions?

Passage-level categorization
Feeling That Trump Will ‘Say Anything,’ Europe Is Less Restrained, Too
HAMBURG, Germany — The Europeans have stopped trying to paper over their differences with President Trump
and the United States.
Traditionally respectful of American leadership and mindful of the country’s crucial role in European defense and
global trade, European leaders normally repress or soften their criticism of United States presidents. But here at
the Group of 20 summit meeting of the world’s industrialized nations, public splits with Mr. Trump were the order of
the day.
Mr. Macron, who waved his iPhone around during the meeting as a symbol of global trade, sharply criticized those
like Mr. Trump who do not support multilateral institutions but push nationalism instead.
“We need better coordination, more coordination,” Mr. Macron said. “We need those organizations that were
created out of the Second World War. Otherwise, we will be moving back toward narrow-minded nationalism.”
Mr. Trump and the British vote to leave the European Union “have proved to be great unifiers for the European
Union,” said Mark Leonard, the director of the European Council on Foreign Relations. “There is a renewed sense
of confidence in Europe after the French election,” the apparent retreat of populism, an increase in economic
growth and the prospect of Ms. Merkel’s re-election in September, he said.
The strains were most visible here on climate policy and trade. Mr. Trump’s withdrawal from the Paris accord was
widely condemned, with Ms. Merkel saying she deplored the move, and all the leaders aside from Mr. Trump
signing up to language that called the agreement “irreversible.” The climate debate in the meeting displayed how
hard it is to isolate the richest, most powerful country in the world.
Prime Minister Theresa May of Britain, her authority weakened at home after a botched election gamble, also tried
to balance Mr. Trump’s deep unpopularity in Britain with her need for American support for the country’s exit from
the European Union and for future trade deals She was criticized for not making the climate issue one of her four
priorities here, and found comfort in Mr. Trump’s promise of a “very powerful” trade deal for a post-“Brexit” Britain
that could be completed “very, very quickly.”
On trade, there was more effort to find compromise, with previous G-20 positions for free trade and against
protectionism watered down to secure American support. The communiqué cited, for the first time, the right of
countries to protect their markets with “legitimate trade defense instruments” — wording that essentially gives Mr.
Trump room to pursue his “America first” policy on issues like steel imports, where Washington is talking about
restrictions based on “national security.”
International politics
G20 summit
Diplomatic conflict
Nationalism
Elections
Climate politics
Brexit
Protectionism

Extraction of semantic relationships
IRVING, Texas, March 2 /PRNewswire/ --
Exxon Mobil Corporation (NYSE: XOM)
announced today that it has reached
agreement to sell Exxon's 130,000 b/d
Benicia, California, refinery and California
fuels marketing assets, and to assign
California supply arrangements, to Valero
Energy Corporation. In addition to the
refinery, the sale involves Exxon's
interest in about 340 service stations.
The assets will be purchased for $895
million plus an amount for inventories and
working capital which will be based on
market-related prices at closing. The
supply arrangements and assets involved
in the Valero Energy Corporation sale
satisfy the conditions required by the
Federal Trade Commission (FTC) and
the State of California, which signed a
parallel consent order and approved the
ExxonMobil merger on November 30,
1999. The final terms of the sale
between ExxonMobil and Valero Energy
Corporation require approval by the FTC
and the State of California.
Sale
agreement
Exxon's Benicia,
California, refinery and
California fuels
marketing assets
Exxon Mobil
Corporation
$895 million plus
an amount for
inventories and
working capital
Valero Energy
Corporation
Seller Buyer
Asset
Price

Discovering the deep
meaning of complex
documents
Deep Semantic Analytics John Smith
Industrial Manufacturing Inc.
Global Technologies Corp.
Theme: Business-Companies
John
Smith
Industrial
Manufacturing Inc.
Global
Technologies Corp.
Has acquired
Is executive
Business-
Mergers&
Acquisitions
Business-
Corporate
executives

APPLICATIONS

Application to contract analysis
 General typification
 Clause identification and
categorization
• E.g.: parts, term, indemnification…
• Present / not
• Anomalous / not
 Extraction of data from clauses
• Identification and role of parties
• Start and end dates, duration
• Governing law
• Liquidated damages

Application to financial report analysis
 Passage/chapter identification and
categorization
• Present / not
• Anomalous / not
 Extraction of data from passages
• Main shareholders: identification and
share volume
• Invested companies: identification and
percentage of ownership
• Concentration of revenue in certain
clients: identification and percentage

Application to news analysis
 Passage-level categorization
 Extraction of meaningful
relationships (e.g., market-
moving information)
• Mergers and acquisitions
• Investments
• Business line sales
• New projects
• New partnership agreements
• New patents

Application to health care information
 Real-World Evidence
• Disease prevalence
• Medication effectiveness
• Behavior and use
• Adverse drug reactions
Clinical records, scientific
publications, specialized
forums…

PRACTICAL CASE

Practical case: due diligence
 Analysis of thousands of
complex documents:
contracts, reports, etc.
 Typically manual task
• Errors
• Discrepancies
• Delays

Due diligence automation (1)
1. Classify by type of document
• Sales Agreement, Employee
Contract, Non-Disclosure
Agreement, Lease Agreement, etc.
2. Analyze each document to detect
and identify its clauses and other
relevant parts
• Title, Parties, Date, Term,
Assignment, Change of Control,
Governing Law, Force Majeure,
Indemnification, Limitation of Liability,
etc.

3. Whithin the different clauses, extract key data and relationships
• Parties → Complete and detailed identification of participants
• Term → Period during which the agreement is effective
• …
Example: term clause
…The term of this Contract
shall commence on January
19, 2017 and continue until
January 18, 2018. …
<contract_dates>
<valid_from>2017-01-19</valid_from>
<valid_to>2018-01-18</valid_to>
</contract_dates>

Example: parties clause
…This Contract is made this 4th
day of January in the year 2017
by and between the BIG BANK
CORPORATION (hereinafter
referred to as the "Purchaser"),
having its principal office at 10
A Street, N.W., Washington
D.C. 20433 and
TechnologyVendor, LLC
hereinafter referred to as the
"Contractor"), a corporation
incorporated under the laws of
the state of New
York having a principal place of
business at 30 Broad Rd, Union
NJ 07083...
<eligible_contract_participants>
<purchaser>
<name>BIG BANK
CORPORATION</name>
<location><address>10 A Street,
N.W.</address><city>Washington
D.C.</city><zip>20433</zip></location>
</purchaser>
<contractor>
<name>TechnologyVendor, LLC</name>
<location><address>30 Broad
Rd</address><city>Union</city><state>
New
Jersey</state><zip>07083</zip></locatio
n>
</contractor>
</eligible_contract_participants>

Example: liquidated damages clause
…Vendor shall pay BIG BANK the
amounts specified below, either in
immediately available funds or as a
credit as elected by BIG BANK in
its sole discretion, within thirty
(30) days after such breach of the
corresponding Vendor obligation:
Vendor Obligation
Amount Payable Upon Breach
Unauthorized Data Access
USD 1,000,000
Unauthorized Data Modification,
Addition, Deletion
USD 1,000,000
Unauthorized Data Disclosure
USD 10,000,000
Unauthorized Transfer of Data
USD 10,000,000…
<obligations>
<obligation><description>Unauthorized
Data Access</description><amount>USD
1,000,000</amount></obligation>
Data Modification, Addition,
Deletion</description><amount>USD
Data
Disclosure</description><amount>USD
Transfer of
Data</description><amount>USD
</obligations>

4. Get valuable insights from the set of documents.

DEEP SEMANTIC ANALYTICS
SOLUTIONS

What would a good solution look like?
 Analyze information in various formats and
presentations: text, PDF, Word, free format, tables
 Accuracy in insight extraction, regardless of domain
 Flexibility in the type of insights to extract and
productivity in its definition
 Multilingual
 High scalability, availability and confidentiality
 Minimum costs and risk in its adoption
 Short time-to-benefit

MeaningCloud: “Meaning as a Service”
(SaaS and on-premises)
Sign up and use it for FREE at
http://www.meaningcloud.com

MeaningCloud’s APIs
Identifies occurrences of
names of people,
organizations, abstract
concepts, quantities, etc.
Theme classification
according to
predefined taxonomies
Identifies general and
attribute-level polarity
Distinguishes among 60
languages
Performs detailed morphosyntactic
analysis
Evaluates the impact of
opinions on several
reputational axes
Discovers meaningful topics
and similarities among texts
without relying on predefined
taxonomies

MeaningCloud APIs for Deep Semantic Analytics
1. Define your model
• Domain ontology
• Semantic (+ syntactic
+ lexical) rules
Text
2. Extract your deep insights
• Passage-level categorization
• Complex patterns
• Semantic relationships
3. Scale
• Quality
• Reliability
• Real-time
• Multilingual

MeaningCloud APIs for Deep Semantic Analytics
 APIs for
• Passage-level categorization
• Extraction of semantic relationships
 Technology based on
• Deep morphosyntactic and
semantic analysis
• Semantic rules to define
categories and relationships
 Benefits:
• Great accuracy
• High productivity in definition, evaluation
and evolution
• Does not require massive training
 In beta in several customers

John Smith
Industrial Manufacturing Inc.
Global Technologies Corp.
Theme: Business-Companies
John
Smith
Industrial
Manufacturing Inc.
Global
Technologies Corp.
Has acquired
Is executive
Business-
Mergers&
Acquisitions
Business-
Corporate
executives
In conclusion
provides great value
Some are already
implementing it

Q & A

Stay tuned to our emails and blog
We’ll be posting a recording of the webinar and
its contents as tutorials soon

Antonio Matarranz, amatarranz@meaningcloud.com
sales@meaningcloud.com
support@meaningcloud.com
http://www.meaningcloud.com
@MeaningCloud
https://www.linkedin.com/company/meaningcloud
Thank you for your attention!
 MeaningCloud LLC
35-37 36th St.
Astoria, New York 11106
+1 (646) 403-3104
 MeaningCloud Europe SL
Llano Castellano 13
28034 Madrid (Spain)
+34 91 3324301

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