About the webinar
The Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organizations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorization of documents, analyze sentiments, improving automatically generated summaries etc.
Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions, and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data
What you will learn
- How organizations are leveraging Named Entity Recognition across various industries
- Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization)
- Best practice to automate machine learning models in hours not months
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
How to analyze text data for AI and ML with Named Entity Recognition
1. 1
How to analyze text data for AI
and ML with Named Entity
Recognition
2. Technology leader with 20+ years expertise in Product
Development, Business strategy and Artificial Intelligence
acceleration. Active contributor in the New York AI
community
Extensively worked with global organizations in BFSI,
Healthcare, Insurance, Manufacturing, Retail and Ecommerce
to define and implement AI strategies
Nisha Shoukath
Co-founder,
People10 & Skyl.ai
The Speaker
3. Extensive experience building future tech products using
Machine Learning and Artificial Intelligence.
Areas of expertise includes Deep Learning, Data Analysis,
full stack development and building world class products in
ecommerce, travel and healthcare sector.
Shruti Tanwar
Lead - Data Science
The Speaker
4. Bikash Sharma
CTO and Co-founder at
Skyl.ai
CTO & Software Architect with 15 years of experience
working at the forefront of cutting-edge technology leading
innovative projects
Areas of expertise include Architecture design, rapid
product development, Deep Learning and Data Analysis
The Panelist
5. Getting familiar with ‘Zoom’
All dial-in participants will be muted to enable the presenters to
speak without interruption
Questions can be submitted via Zoom Questions chat
window and will be addressed at the end during Q&A
The recording will be emailed to you after the webinar
Please familiarize yourself with the Zoom ‘Control Panel’ on your screen
6. Live Demo on
Customer Reviews
Moderation using
NER
How organizations
are leveraging
Named Entity
Recognition
How to quickly
overcome the
challenges in
building ML models
1 2 3
...In the next 45 minutes
7. Machine Learning automation platform for unstructured data
A quick intro about Skyl.ai
Guided Machine Learning Workflow
Build & deploy ML models faster on
unstructured data
Collaborative Data Collection & Labeling
Easy-to-use & scalable AI SaaS platform
8. POLL #1
At what stage of Machine learning adoption your
organization is at?
⊚ Exploring - Curious about it
⊚ Planning - Creating AI/ML strategy
⊚ Experimenting - Building proof of concepts
⊚ Scaling up - Some departments are using it
⊚ In production - Using it in product features
⊚ Transforming - AI/Ml driven business
10. 80% of worldwide data will
be unstructured by 2025
- IDC
Examples of unstructured data
Text files Audio files
Images Web pages
Video files Emails
11. Challenges with unstructured text data
⊚ Large Archives or records of data
⊚ Extracting hidden information needs manual efforts
⊚ Traditional rule based system can’t keep up with new changes
12. NER : Extract phrases in text that refer to real-world entity
Eg: Kimberley will be traveling to New York on Thursday
People - Kimberley
Place - New York
Time - Thursday
‘Named Entity Recognition’ to the rescue!
13. ⊚ Identify and extract relevant
information like aggressive
clauses, legal anomalies, future
financial obligations, renewal or
expiration dates, and even
summarise contract data down
to concise points.
Legal - Contract Analysis
Contract Title
Start Date
Contracting
Parties
14. ⊚ Extract skills, education, and
experience details of candidate
resumes/CVs
⊚ Check the extracted information
with the criteria of job description
and list preferable candidates
accordingly
⊚ Removes subconscious bias
HR and Recruitment - Profile Evaluation
15. ⊚ Extract information like delivery
address, vendor names, product
details, quantity, and pricing from
these documents.
⊚ Using the extracted data, AI can
match PO’s with their Invoices and
ORN’s, maintaining transaction
consistency.
Manufacturing - Procurement Matching
(Invoices, order receipt notes,…)
16. Biomedical - Research & Analysis
⊚ Understanding the correlation
between drugs and diseases, genes
and diseases etc.
⊚ Drug Discovery
⊚ Extraction of disease from
electronic health records
17. ⊚ Extract opinion or related
product mentions which may help
the seller and consumer to analyze
from 100s product review into
meaningful review mentions and
derive business actions.
Ecommerce - Customer Review Moderation
Review for Canon EOS 6D Mark II
26.2MP Digital SLR Camera
21. POLL #2
Some challenges that you are facing while
implementing AI & Machine Learning
⊚ Not started yet, so no challenges
⊚ Data collection
⊚ Data Labeling
⊚ Large volumes of data
⊚ Identifying the right data set to
train
⊚ Data Security
⊚ Lack of knowledge of ML tools
⊚ Lack of end to end platform
⊚ Lack of expertise
⊚ Choosing the right algorithms
23. Data Collection - Flexible options
(CSV bulk upload, APIs, Mobile capture, Form based…)
24. Data Labeling - Simple 4 steps process
(guided workflows, collaboration jobs,…)
25. Data Labeling - Real-time early visibility
(class balance, missing data…)
26. Data Labeling with Effective Collaboration
(Job allocation, trends, statistics, interactive messaging…)
⊚ Analyse trends and
progress of your data labeling
job in real time with statistics
and interactive visualizations
⊚ Manage collaborator
progress, activity, interactive
messaging
27. ⊚ On-prem solutions - Data stays in your own
servers, and in your own databases, giving you complete
control over your data.
⊚ Controlled access flow - Defined and controlled
access flow allows selective restriction so that you have full
command to regulate who can view or use resources in
your ML projects.
⊚ Encrypted data sources - All data sources are
encrypted in Skyl thus giving users an additional layer of
security, making sure your data stays safe and protected.
DataSecurity- on premise solutions
(encrypted data sources, access controlled flow..)
28. Data Visualization to build strong data intuition
(visuals for data composition, data adequacy...)
29. One click training
(Easy feature sets, out of the box algorithms, API integration, hyper
parameter tuning, auto scaling…)
⊚ Train, Deploy and Version your models by
creating feature-sets in no time with our easy
feature selection provision.
⊚ Choose from state-of-art neural network
algorithms, tune hyperparameters and see logs
for your training in real time.
⊚ Integrate powerful inference API with your
application for AI-driven actionable intelligence.
⊚ Auto scaling of model training based on
data and hyperparameters.
30. ⊚ Monitor your deployed models
and analyse inference count,
accuracy and execution time.
⊚ See how your models are
performing in real-time. No black
boxes here.
Model Evaluation - Release Confidently
(Accuracy, Precision, Recall, F1 Score)
31. Model Monitoring of metrics in real-time
(inference count, execution time, accuracy…)
⊚ Monitor your deployed models and analyse inference count, accuracy and execution time.
⊚ See how your models are performing in real-time. No black boxes here.
32. No upfront cost in Infrastructure set up
(no DevOps needed, auto-deploy, SaaS & On-prem models…)
No DevOps
required
01
Latest tech
stack
02
On premise
and saas
models
03
Scalable
On
demand
04
33. Skyl.ai - as ML automation platform
Efficient
Data Management
Solve your data issues; collect and manage data
efficiently
Accuracy
& Quality
Maintain accuracy and quality; train and test faster;
monitor quality
Effective
Collaboration
Collaborate and manage projects efficiently
Early
Visibility
Get early visibility; visualize and affirm correctness
on every step of the way
Scalable
High - Performance
Access on-demand and scalable, high-performance
infrastructure
Reduce
Cost
Reduce cost of implementation; do it with less
specialized resources
34. ⊚ Free 1 month Trial + POC
⊚ Complimentary 30 min consultation
⊚ AI Implementation Playbook
www.skyl.ai contact@skyl.ai
Special offer for you...
36. 36
We hope to hear from you soon
Thank you for joining!
85 Broad Street, New York, NY, 10004
+1 718 300 2104, +1 646 202 9343
contact@skyl.ai
Notes de l'éditeur
Hello everyone and welcome. Thank you for joining today’s webinar on How to analyze text data, for AI and ML, with Named Entity Recognition. My name is Edwin and I’ll be your host today. First off, I’d like to introduce 3 expert speakers for today’s webinar..
First we have Nisha Shoukath - Nisha is a technology entrepreneur with background in investment banking.
She’s co-founded two successful technology startups and has worked with wide variety of global organizations from different industries.
She helps enterprises with defining AI strategy, and AI adoption roadmaps. Welcome, Nisha!
Next we have Shruti Tanwar - Shruti is an expert in data science who is a veteran in building SaaS products using Machine Learning and AI.
Her expertise includes Deep Learning and Data Analysis, as well as full stack development and building tech products in various different fields such as ecommerce, travel, and healthcare. Welcome, Shruti!
Finally, we have Bikash Sharma joining today.
Bikash is CTO and Software Architect with 15 years of experience in leading innovative software projects and solutions.
He’s co-founded Skyl with his expert knowledge in AI and Machine Learning. Welcome, Bikash!
Before we begin, I’d like to briefly talk about some relevant Zoom features.
All participants in the webinar will be muted to avoid any interruptions during the session.
Any questions you might have can be submitted to the Zoom Questions chat window in the control panel, located on the bottom of the screen.
We’ll make sure to address your questions during the Q&A session.
Also, the recording of the webinar will be emailed to you afterwards, just in case you’ve missed any talking points or wish to view it again.
So that’s all for the introduction - now we’ll get started with the webinar and I’ll hand over the session to Nisha
Exploring - Curious about it
Planning - Creating AI/ML strategy
Experimenting - Building proof of concepts
Scaling up - Some departments are using it
In production - Using it in product features
Transforming - AI/Ml driven business
NER also known as entity chunking and entity identification is a process where an algorithm takes a string of text (sentence or paragraph) as input and identifies relevant nouns (people, places, and organizations) that are mentioned in that string.
In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match.
In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match.
In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match.
1.Mining these biological associations from literature can provide immense support to research ranging from drug-targetable pathways to biomarker discovery. However, time and cost of manual curation heavily slows it down
Through NER, certain hidden information in the diagnosis could be dug out and further contribute to improving existing medical systems. More importantly, medical information processing systems that rely solely on structured data are unable to directly access such kinds of hidden information in the medical text.
In procurement, both the buyers and vendors have to ensure that the documentations remain consistent in the transaction. The contents of purchase orders, invoices, and order receipt notes, etc. have to match.
How
5 minutes intro - 10 industry awareness - 15 min demo - 20 minutes QnA
Define problem - Features model - How this model is built using skyl.ai
Add slide of Pneumonia detection
Not started yet, so no challenges
Data collection
Data Labeling
Data Bias
Large volumes of data
Identifying the right data set to train
Lack of knowledge of ML tools
Lack of end to end platform
Lack of expertise
Choosing the right algorithms
Monitoring the model performance
Benefit
On-prem solutions
For industries, where business depends upon sensitive data, Skyl provides the provision of on-prem solutions. Your data stays in your own servers, and in your own databases, giving you complete control over your data.
Access controlled flow
Defined and controlled access flows with different organizational roles like business owner, project lead, collaborators etc. allow for selective restriction so that you have full command to regulate who can view or use resources in your ML projects.
Encrypted data sources
All data sources are encrypted in Skyl thus giving users an additional layer of security, making sure your data stays safe and protected.
Now, we
Thank you Nisha and Shruti, for the wonderful presentation and demo.
As mentioned earlier, the recording of the webinar will be emailed to you afterwards. [pause]
Before we get to the Q&A, I want to mention some of the offers Skyl has for those of you that are curious about incorporating Machine Learning to your business.
Skyl offers a free 1 month trial, plus Proof of Concept.
You’ll be able to interact with real data on the screen, just like we showed in the demo. You’ll experience the process of going from collecting & labeling the data… all the way to deploying a model!
Skyl also offers a complimentary 30 min consultation and an AI Implementation Playbook to go along.
This is a great opportunity to see how Skyl can provide Machine Learning solutions to your challenges.
If you’re interested in finding out more, please visit the skyl.ai website or you can send an email directly to contact@skyl.ai.
Alright, now it’s Q&A time!
As a reminder, if you have any questions, go to the question box in your control panel - located on the bottom of your Zoom screen.
We’ll try to answer as many questions as possible in the time that we have left.
So let’s answer some questions.
Sample questions:
Shruti
- (Anonymous) Regarding text, should it be written text or can it be scanned copies and the algorithm will identify the words in the scanned item?
-(Julie) How much is the devops effort in building a model deployment pipeline in Skyl?
- (Aaron) How can I keep track of my model’s performance and fairness?
Nisha
-(Vikas) Do you have in-prem facility?
-(anonymous) What about data pre-processing?
-(Alem) Apart from text, can I use Skyl for image based data, like screenshots, to build model for my customer center??
Ok, that’s all the time we have for questions today, but feel free to contact us with your specific questions and we’ll make sure to get them answered.
All right, so we have reached the end of the webinar.
We hope you enjoyed it.
We have a lot more webinars coming up on different machine learning topics and how they can be implemented into different businesses and industries,
So don’t miss out and make sure you sign up for upcoming webinars as well
Thank you for joining and I hope you have a wonderful day.