1. The document discusses applying natural language processing (NLP) to enterprise search to make it more cognitive and intent-based. It provides examples of how NLP can interpret text, analyze sentiment, and understand intent to improve search results. 2. The document also describes how Capgemini is helping customers evolve their enterprise search capabilities by developing cognitive search solutions using NLP and technologies like Azure, Office 365, and SharePoint. This provides more accurate, efficient search and knowledge retrieval. 3. A demo is provided of a cognitive search solution developed for a consulting firm customer to enable rapid access to expertise data and empower consultants to quickly serve clients.

1. CW
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CAPGEMINI
WEEK OF
INNOVATION
NETWORKS
1. Artificial Intelligence

2. CW
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CAPGEMINI
WEEK OF
INNOVATION
NETWORKS
NLP & Enterprise Search
Shailesh S. Agaskar/Omkar Vahalkar
Mumbai
26th September 2018

3. 3NLP & Enterprise Search © 2018 Capgemini. All rights reserved.
Table Of Contents
Cognitive Dimension to Enterprise Search
NLP(Natural Language Processing)
Applied NLP to Enterprise Search
01
02
03
04
Enterprise Search Evolution
How are we helping our Customers05
Demo06

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Enterprise Search Evolution
Yesterday Today Tomorrow
• Limited Data Sources
• Purely Indexed based
• Single item match based result sets
• Relevancy based.
• Unstructured content e.g. Content in
Documents , Web pages etc.
• Multiple Data Sources.
• Indexed & Intent based.
• Personal activity based results.
• Generate Insights from the content.
• Unstructured and Structured Content
including social media and images.
• Driving Actionable insights.
• Indexed , Intent & Natural Human
Interaction
• Decision based on intent across
many facets of data.
• Entity Extraction.& AI for semantic
understanding.
• Driven by organization priorities and
extended network
➢ Enterprise Search enables content from multiple sources within
enterprise to be searchable to find relevant data.
➢ Enterprise Search evolved from keyword based to Cognitive
Search.

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01
02
03
Intent, Context & Speed
leading to better insights and
informed decisions.
Cognitive Dimension to
Enterprise Search Challenges
▪ Traditional Keyword based search is obsolete as no business
value in the world of speedy business decisions.
▪ Inaccurate results and multiple iterations of search to arrive
at the required results.
▪ Diverse and Big Data sets not in Enterprise Search engines
lead to missing insights.
Solutions
▪ Diverse and Big Data sets leveraged for insights to drive
actionable next steps and decisions.
▪ Cognitive dimension provides user’s intent and interest
based results for speedy, relevant and accurate results.
▪ AI based cognitive search with self-learning algorithms
which continue to learn and be mature to deliver more
relevant results.
Benefits
▪ Results based on customer requirements.
▪ Knowledge Teams better utilized in quality work
▪ More relevant , speedy and accurate with more
empowerment
▪ All round view of the customer helps provide in-depth
insights driving smarter informed decisions.

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Cognitive Search Roadmap
* Image from Forrester Report Q2 2017

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Interpret & Analyze Text
• Spoken words are
interpreted.
• Important keywords are
extracted.
• Keywords are used to
surface relevant results.
• Achieves accuracy with
context.
Sentiment Analysis
Intent based Content
NLP(Natural Language Processing)
• Also known as Opinion Mining
• Analyze the content and guage
the sentiment.
• Primarily Social Media e.g Blogs,
Tweeter feeds etc.
• Based on the sentiment analysis
bring about changes to your
business e.g.Branding, Product
prioritization etc.
Natural Language Processing is the ability of a computer program to
understand human language as it is spoken and apply to various use
cases.
Use Cases
• Cognitive Search.
• Conversational Retail
• Translators
• Bot interactions.
• Healthcare.
• Customer Service.
• Many More....

8. NLP & Enterprise Search | 26th Sept 2018 © 2018 Capgemini. All rights reserved. 8NLP & Enterprise Search | 26th Sept 2018 © 2018 Capgemini. All rights reserved.
Applied NLP to Enterprise Search
Bots
End Users
Data
Scientists
Search
Platform
NLP Engine
& trained models
Machine
Learning
Database
Cloud
Business
Applications
Social Media

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Search Platforms evolving to Cognitive Searches
* Image from Forrester Report Q2 2017

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Customer Ambition
• Vision 2020 to be Digital
Enterprise enabling close
collaboration with
customers.
• Empower consultants with
most efficient tools to make
insights readily available.
• Enable consultants to take
decision quickly so as to
serve the customer
efficiently
Solution
How are we helping our Customers
• Knowlege assets gathered and
maintained across the years of
data leveraged.
• O365 & SharePoint Online
primary Enterprise Platforms.
• Federation across diverse data
sources.
• Single point of knowledge
retrieval point accessible across
multiple devices with voice and
text enablement.
Engagement with one of the top Consulting Firms helping them to
On-Board on Digital Enterprise Journey.
Benefits
• Intent based accurate and
efficient data retrieval.
• Enterprise wide skills being
mined to engage right
consultants for the specific
engagements.
• Enterprise Search being
replaced by the Cognitive with
seamless User Experience.
• Future Roadmap to become
Cognitive enabled Enterprise.

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How are we helping our Customers
Language
Understanding
Intelligent
Service(LUIS)
Bing
Speech API
Expertise Data Sources
Search Single Page
App
Speech →
 Text
Utterances →
 Intent , Entities
Search Keywords
Search Query Federation
Query Text Search Results
▪ Combination of Azure + O365 +
SharePoint Online.
▪ Microsoft LUIS framework used for
NLP.
▪ Bing Speech API used for Voice to
Text.
▪ Pilot done for 1000 users across 2
regions.
▪ Next Beta for 10000 users across
more regions based on
Organizational Compliance.
▪ LUIS Model trained based on
patterns gathered through
Webtrends.
▪ Responsive User Interface available
across multiple devices.

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Demo

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14. NLP & Enterprise Search | 26th Sept 2018 © 2018 Capgemini. All rights reserved.
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Copyright © 2018 Capgemini. All rights reserved.
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