Vaklipi Text Analytics Tools

Bangalore, India
A Platform for Text Analytics
Vaklipi
http://www.vaklipi.com
Aiaioo Labs vaklipi.com

Aiaioo Labs
• Research Lab
– Natural Language Processing
– Machine Learning
– Information Retrieval
• Vaklipi
– Vaklipi is the brand under which our text
processing tools are distributed.
– Vak means Speech or Spoken Language
– Lipi means Writing or Written Language

What can Vaklipi help you get out of text?
The opportunities vary by vertical:
1. Aerospace / Defense / Automotive – Compliance process automation /
Technical specification standardization / Competitive intelligence and
customer feedback processing
2. Healthcare / Life sciences – Reporting / Storing, retrieving and analyzing
patents and publications / Extraction of actionable insights from research
and competitive intelligence
3. Legal and Government – Legal and administrative filings / Case document
and administrative record management, e-discovery / Extraction of facts
from legal and administrative documents (land records, case files)
4. FMCG – Routine reports, invoicing, tax documents / Sentiment and
Intention Analysis / Call center insights
5. BFSI – Reporting / Churn and product propensity analysis / Forms
processing, customer care, monitoring, fraud detection

How do we unlock
the value in “text”?
Output Text / Store and Analyse Text / Ingest Text
d. Vaklipi Generation Tools
a. Vaklipi Extraction Tools
b. Vaklipi Classification Tools
c. Vaklipi Exploration Tools

Vaklipi Extraction Tools
“Extraction”

a. Vaklipi Extraction Tools
Use Cases:
1. Customer Service
2. Land Records
3. Mergers and Acquisitions
4. Sentiment Analysis
5. Intention Analysis
6. Sales Lead List Extraction
(4, 5 and 6 are supported out of the box, while
1, 2 and 3 will require customization)

Use Case 1: Customer Service
Let’s say you have some text … … and a database or spreadsheet with columns
“John Chambers of Springfield, MA
reported a problem with the clutch
on his Ford Ranger purchased in
Boston, MA in 2005.”
… and you have to fill in the database fields
from the information in the text …
Reporter Location (of
Reporter)
Product

Use Case 1: Customer Service
Entities are pieces of text that could go into the fields in the database.
Relations connect the entities that belong in a row.
Reporter Location Product
John
Chambers
Springfield,
MA
Ford
Ranger

Use Case 2: Government – Land Records
“Property K45L234
(lot 23-24) in Wake County
of 3000 sq ft
was sold to James Fischer
on 3-30-1997 …”
Title# Lot County

Use Case 3: Legal Domain – Mergers/Acquisitions
“Acme Financials, a subsidiary
of Lehman Sisters, was acquired
by John Doe Corp on 5/26/2001.”
Acquirer Acquired Date

Use Case 4: Sentiment Analysis
“The Canon 3F model is sturdy
and excellent for studio use.”
Product Sentiment Purpose
Canon 3F Positive Studio
Use

Use Case 4: Sentiment Analysis
Live demo: http://www.aiaioo.com:8080/annotator-0.1/automation/demoView/1

Use Case 4:
Sentiment Analysis is available in many languages

Use Case 5: Intent Analysis
“I want to learn more about
the premium plan you have
for frequent fliers.”
Product Intent Constraint
Premium
plan
Inquire Frequent
fliers

Use Case 5: Intent Analysis

Use Case 6: Sales Lead List Extraction
“Tim Cook is the CEO of Apple a firm in the computer industry…”
… and you have to build a leads list of CEOs by industry …
CEO Firm Industry
Tim Cook Apple Computers

Use Case 6:
Sales Function – Sales Lead List Extraction
Video demonstration: http://selasdia.com/lead-finder/
(Selasdia is the brand of a robotic sales assistant developed on the Vaklipi platform)

Use Case 6: CXO and Email Finder
Video demonstration: http://selasdia.com/cxo-finder/
(Selasdia is the brand of a robotic sales assistant developed on the Vaklipi platform)

Vaklipi Classification Tools
Type = “Tools”

b. Vaklipi Classification Tools
Use Cases:
1. Problem Routing (Assignment)
2. Topic Classification
3. Sentiment Classification
4. Subjectivity Classification
5. Intention Classification
6. Language Identification
7. Gender Identification
(3, 4, 5, 6 and 7 are supported out of the box, while
1 and 2 will require customization)

Use Case 1: Problem Routing
Let’s say you have some text … … and you want to mark it as one of …
… and so route to customer service or sales
Report
Inquiry

Use Case 2: Topic Classification
Let’s say you have a news item… … and you want to mark it as one of …
“Luxembourg Foreign Minister Jean Asselborn
has called for Hungary to be suspended or
even expelled from the European Union
because of its "massive violation" of EU
fundamental values..”
… and so decide the news category
Politics
Sports

Use Case 3: Sentiment Classification
Let’s say you have a review … … and you want to mark it as one of …
“WOW...that's all I can say. I've been waiting for
this one to come out on Blu-ray for quite awhile...
and I can truly say, it was worth the wait. Finally,
this movie looks like it did in the theater...the colors,
details and clarity are amazing...”
… and so decide the news category
Positive
Negative

Use Case 4: Subjectivity Classification
You can use Vaklipi to decide if there is an opinion/sentiment in the
sentence in the first place …
“I am sad that Steve Jobs died.” Subjective
“Steve Jobs died.” Objective

Use Case 5: Intent Classification
We provided an intent classifier with Vaklipi …
“Who has a typewriter for sale?”
• Purchase intention

Use Case 6: Language Identification
(A very accurate language identifier is provided with Vaklipi) …

Use Case 7: Gender Identification
(A multilingual gender identifier is provided with Vaklipi) …
Male Female
Arun
Ashwin
Akshata
Aparna
Out-of-the-box models available

All the above use cases illustrate a
technique called Classification
There are two types of classification supported by the Vaklipi
platform:
1. Classification using Machine Learning
2. Classification using Linguistic Resources (Gazetteers)
Advantages of using the Vaklipi platform:
1. Several pre-built classifiers available out of the box
2. Support for rapid development of new classifiers

Vaklipi Exploration Tools

c. Vaklipi Exploration Tools
Use Cases:
1. Automatic Label Generation for Grouping / Retrieval
2. Frequent Issue Detection
3. Key Issue Detection
4. Churn Prediction
5. Product Propensity Analysis
6. Shift Detection
7. Summarization
8. Bootstrapping Extraction Tools
(All are supported out of the box – the raw data will need to
be set up for the tool but no training data is needed)

Use Case 1: Data Organization
ABSTRACT
A challenging problem faced by researchers and
developers of distributed real-time and embedded (DRE)
systems is devising and implementing effective adaptive
resource management strategies that can meet end-to-
end quality of service (QoS) requirements in varying
operational conditions. This paper presents two
contributions to research in adaptive resource
management for DRE systems.
How do you label/organize the following research abstract?

1. adaptive resource management
2. distributed real-time embedded system
3. end-to-end quality of service
4. service end-to-end quality
5. hybrid adaptive resourcemanagement middleware
6. hybrid control technique
7. real-time video distribution system
8. real-time corba specification
9. video encoding/decoding
10. resource reservation mechanism
11. dynamic environment
These tags that were chosen by
a human.

1. resource utilization
2. base station
3. qos requirements
4. dre multimedia
5. a/v streaming service
6. frame rate
7. end-to-end qos requirements
8. adaptive resource management
9. network bandwidth
10. application qos
11. picture resolution
These tags that were chosen by
a machine.

Use Case 2: Frequent Issue Detection
The customer complained about
delays in processing incoming
international payments ...
Textual Events
I helped the customer recover
his lost password after he was
locked out of his account ...
Method:
Take a bunch of textual events
such as these and reduce them
to a set of issues such as:
a) international payment delays
b) account lockout

Use Case 3: Key Issue Detection
The customer complained about
delays in processing incoming
international payments ...
Textual Events
I helped the customer recover
his lost password after he was
locked out of his account ...
Method:
Take a bunch of
textual events such
as these that are
correlated with
adverse business
events and reduce
them to a set of
issues such as:
a) payment delay
b) account lockout
Account balance < Rs.
1,00,000
Adverse Business Events
Online account usage
frequency drops to 0 for
3 months.

Use Case 4: Churn Analysis
Applications to Churn Analysis
The customer is having a lot
of issues with the online
portal.
Textual Events
The customer was locked out
of his account again ...
Account balance < Rs.
1,00,000
Adverse Business Events
Online account usage
frequency drops to 0 for
3 months.

Use Case 5: Purchase Propensity Analysis
Applications to Purchase Propensity Analysis
The customer inquired about
accessing investment banking
services.
Textual Events
Customer requested a credit
line.
New investment made
Propitious Business Events
Increased safe use of
credit line.

Use Cases 6: Shift Detection
Shift detection is a form of trend analysis useful for
marketing or planning.
Category Leaders:
2010
1) Marketo
2) Radian6
3) Pardot
4) Alchemy
5) Hubspot
6) Act-On
7) Lexalytics
Category Leaders:
2015
1) Hubspot
2) Marketo
3) Act-On
4) Salesforce
5) IBM
6) Oracle
7) Adobe

Use Case 7: Summarizing Feedback
Applications to processing large interaction streams

Vaklipi Generation Tools

d. Vaklipi Generation Tools
Use Cases:
1. Text Entry Assistance
2. Smart User Interfaces
3. Customer Support Assistance
4. OCR/Speech Output Correction
5. Question Answering
6. Summarization
(These require training data and extensive customization
and are mostly used where operational efficiency is
important)

Use Case 1: Text Entry Assistance (Auto-Suggest)
How do you help a text entry operator make fewer mistakes?
Vaklipi allows you to develop models which predict what someone is
about to type.
These can be integrated into user interfaces to create auto-suggest systems.

Use Case 2: Smart User Interfaces
How do you guide a user to use a machine learning tool so that
it makes fewer mistakes?
Vaklipi allows you to develop models that can guide usage intelligently.
These require an integration with user interfaces.

Use Case 3: Customer Support Assistance
How do you guide a customer support person to answer
questions faster?
Vaklipi allows you to develop answer retrieval and customization systems that
can make the job of a CSR easier.

Use Case 4: OCR/Speech Output Correction
How do you correct the output of a machine learning tool so
that it makes fewer mistakes?
Vaklipi allows you to develop models that can correct other
machine learning algorithms.
Source Channel
Decoder

Use Case 5:
Answering Questions
First you classify the question into one of 3 types… and these are…
“Do you ship your cars to Boston, MA?”
“Who is the CEO of Apple?”
“Why is the sky blue?”
Yes/No questions
Factoid questions
Non-factoid questions

Use Case 5:
Answering Questions
Look for answers in databases that you created using entity / relationship extraction
“Do you ship your cars to Boston, MA?”
“Who is the CEO of Apple?”
“Why is the sky blue?”
Product Ships To
Cars USA
CEO Firm
Tim
Cook
Apple

Use Case 5:
Answering Questions (Who is the CEO of Adobe?)
Video Demo: http://selasdia.com/cxo-finder/

Use Case 6: Summarization
Useful for processing / distilling large interaction streams

THE END
Bangalore, India
http://www.vaklipi.com

Vaklipi Text Analytics Tools

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