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Building an Enterprise Chatbot: Work with Protected Enterprise Data Using Open Source Frameworks 1st ed. [Pehme köide]

  • Formaat: Paperback / softback, 385 pages, kõrgus x laius: 235x155 mm, kaal: 623 g, 102 Illustrations, black and white; XXII, 385 p. 102 illus., 1 Paperback / softback
  • Ilmumisaeg: 13-Sep-2019
  • Kirjastus: APress
  • ISBN-10: 1484250338
  • ISBN-13: 9781484250334
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  • Formaat: Paperback / softback, 385 pages, kõrgus x laius: 235x155 mm, kaal: 623 g, 102 Illustrations, black and white; XXII, 385 p. 102 illus., 1 Paperback / softback
  • Ilmumisaeg: 13-Sep-2019
  • Kirjastus: APress
  • ISBN-10: 1484250338
  • ISBN-13: 9781484250334
Explore the adoption of chatbots in business by focusing on the design, deployment, and continuous improvement of chatbots in a business, with a single use-case from the banking and insurance sector. This book starts by identifying the business processes in the banking and insurance industry. This involves data collection from sources such as conversations from customer service centers, online chats, emails, and other NLP sources. You’ll then design the solution architecture of the chatbot. Once the architecture is framed, the author goes on to explain natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG) with examples. 

In the next sections, you'll design and implement the backend framework of a typical chatbot from scratch. You will also explore some popular open-source chatbot frameworks such as Dialogflow and LUIS. The authors then explain how you can integrate various third-party services and enterprise databases with the custom chatbot framework. In the final section, you'll discuss how to deploy the custom chatbot framework on the AWS cloud.


By the end of Building an Enterprise Chatbot, you will be able to design and develop an enterprise-ready conversational chatbot using an open source development platform to serve the end user.

What You Will Learn
  • Identify business processes where chatbots could be used
  • Focus on building a chatbot for one industry and one use-case rather than building a ubiquitous and generic chatbot 
  • Design the solution architecture for a chatbot
  • Integrate chatbots with internal data sources using APIs
  • Discover the differences between natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG) 
  • Work with deployment and continuous improvement through representational learning

Who This Book Is For
Data scientists and enterprise architects who are currently looking to deploy chatbot solutions to their business.

About the Authors xiii
About the Technical Reviewer xvii
Acknowledgments xix
Introduction xxi
Chapter 1 Processes in the Banking and Insurance Industries 1(18)
Banking and Insurance Industries
1(5)
A Customer-Centric Approach in Financial Services
6(3)
Benefits from Chatbots for a Business
9(1)
Chatbots in the Insurance Industry
10(4)
Automated Underwriting
12(1)
Instant Quotations
13(1)
Al-Based Personalized Experience
13(1)
Simplification of the Insurance Buying Process
13(1)
Registering a Claim
13(1)
Finding an Advisor
13(1)
Answering General Queries
14(1)
Policy Status
14(1)
Instant Notifications
14(1)
New Policy or Plan Suggestions
14(1)
Conversational Chatbot Landscape
14(3)
Summary
17(2)
Chapter 2 Identifying the Sources of Data 19(16)
Chatbot Conversations
19(2)
General Conversations
20(1)
Specific Conversations
20(1)
Training Chatbots for Conversations
21(6)
Self-Generated Data
22(1)
Customer Interactions
23(2)
Customer Service Experts
25(1)
Open Source Data
26(1)
Crowdsourcing
26(1)
Personal Data in Chatbots
27(2)
Introduction to the General Data Protection Regulation (GDPR)
29(4)
Data Protected Under the GDPR
29(1)
Data Protection Stakeholders
30(1)
Customer Rights Under the GDPR
30(2)
Chatbot Compliance to GDPR
32(1)
Summary
33(2)
Chapter 3 Chatbot Development Essentials 35(20)
Customer Service-Centric Chatbots
35(7)
Business Context
36(2)
Policy Compliance
38(1)
Security, Authentication, and Authorization
39(2)
Accuracy of User Input Translation to Systems
41(1)
Chatbot Development Approaches
42(7)
Rules-Based Approach
43(2)
Al-Based Approach
45(2)
Conversational Flow
47(2)
Key Terms in Chatbots
49(3)
Utterance
49(1)
Intent
50(1)
Entity
50(1)
Channel
51(1)
Human Takeover
51(1)
Use Case: 24x7 Insurance Agent
52(1)
Summary
53(2)
Chapter 4 Building a Chatbot Solution 55(16)
Business Considerations
55(3)
Chatbots vs. Apps
56(1)
Growth of Messenger Applications
57(1)
Direct Contact vs. Chat
57(1)
Business Benefits of Chatbots
58(2)
Cost Savings
58(1)
Customer Experience
59(1)
Success Metrics
60(1)
Customer Satisfaction Index
60(1)
Completion Rate
60(1)
Bounce Rate
61(1)
Managing Risks in Chatbots Service
61(2)
Third-Party Channels
61(1)
Impersonation
62(1)
Personal Information
62(1)
Confirmation Check
63(1)
Generic Solution Architecture for Private Chatbots
63(6)
Workflow Description
64(3)
Key Features
67(1)
Technology Stack
68(1)
Maintenance
68(1)
Summary
69(2)
Chapter 5 Natural Language Processing, Understanding, and Generation 71(122)
Chatbot Architecture
73(3)
Popular Open Source NLP and NLU Tools
76(6)
NLTK
77(1)
spaCy
77(2)
CoreNLP
79(1)
gensim
80(1)
TextBlob
81(1)
fastText
82(1)
Natural Language Processing
82(34)
Processing Textual Data
83(2)
Word Search Using Regex
85(1)
Word Search Using the Exact Word
86(1)
NLTK
87(7)
spaCy
94(12)
CoreNLP
106(4)
TextBlob
110(3)
Multilingual Text Processing
113(3)
Natural Language Understanding
116(25)
Sentiment Analysis
117(1)
Language Models
118(15)
Information Extraction Using OpenIE
133(3)
Topic Modeling Using Latent Dirichlet Allocation
136(5)
Natural Language Generation
141(27)
Markov Chain-Based Headline Generator
142(3)
SimpleNLG
145(7)
Deep Learning Model for Text Generation
152(16)
Applications
168(23)
Topic Modeling Using spaCy, NLTK, and gensim Libraries
169(6)
Gender Identification
175(4)
Document Classification
179(5)
Intent Classification and Question Answering
184(7)
Summary
191(2)
Chapter 6 A Novel In-House Implementation of a Chatbot Framework 193(88)
Introduction to IRIS
194(1)
Intents, Slots, and Matchers
195(17)
Intent Class
197(1)
IntentMatcherService Class
198(3)
The getlntent Method of the IntentMatcherService class
201(4)
Matched Intent Class
205(2)
Slot Class
207(5)
IRIS Memory
212(7)
Long- and Short-Term Sessions
212(1)
The Session Class
213(6)
Dialogues as Finite State Machines
219(11)
State
221(1)
Shields
222(1)
Transition
223(1)
State Machine
224(6)
Building a Custom Chatbot for an Insurance Use Case
230(48)
Creating the Intents
233(10)
IrisConfiguration
243(28)
Managing State
271(2)
Exposing a REST Service
273(4)
Adding a Service Endpoint
277(1)
Summary
278(3)
Chapter 7 Introduction to Microsoft Bot, RASA, and Google Dialogflow 281(22)
Microsoft Bot Framework
281(11)
Introduction to QnA Maker
282(8)
Introduction to LUIS
290(2)
Introduction to RASA
292(4)
RASA Core
294(1)
RASA NLU
295(1)
Introduction to Dialogflow
296(5)
Summary
301(2)
Chapter 8 Chatbot Integration Mechanism 303(42)
Integration with Third-Party APIs
303(18)
Market Trends
304(6)
Stock Prices
310(6)
Weather Information
316(5)
Connecting to an Enterprise Data Store
321(4)
Integration Module
325(13)
Demonstration of Asklris Chatbot in Facebook Messenger
338(6)
Account Balance
338(1)
Claim Status
339(1)
Weather Today
340(1)
Frequently Asked Questions
341(1)
Context Switch Example
342(2)
Summary
344(1)
Chapter 9 Deployment and a Continuous Improvement Framework 345(32)
Deployment to the Cloud
345(12)
As a Stand-Alone Spring Boot JAR on AWS EC2
346(3)
As a Docker Container on AWS EC2
349(3)
As an ECS Service
352(5)
Smart IRIS Alexa Skill Creation in Less Than 5 Minutes
357(11)
Continuous Improvement Framework
368(7)
Intent Confirmation (Double-Check)
369(2)
Predict Next Intent
371(2)
A Human in the Loop
373(2)
Summary
375(2)
Index 377
Abhishek Singh is on a mission to profess the de facto language of this millennium, the numbers. He is on a journey to bring machines closer to humans, for a better and more beautiful world by generating opportunities with artificial intelligence and machine learning. He leads a team of data science professionals solving pressing problems in food security, cyber security, natural disasters, healthcare, and many more areas, all with the help of data and technology. Abhishek is in the process of bringing smart IoT devices to smaller cities in India so that people can leverage technology for the betterment of life.





He has worked with colleagues from many parts of the United States, Europe, and Asia, and strives to work with more people from various backgrounds. In 7 years at big corporations, he has stress-tested the assets of U.S. banks at Deloitte, solved insurance pricing models at Prudential, and made telecom experiences easier for customers at Celcom, and core SaaS Data products at Probyto. He is now creating data science opportunities with his team of young minds.





He actively participates in analytics-related thought leadership, authoring, public speaking, meetups, and training in data science. He is a staunch supporter of responsible use of AI to remove biases and fair use of AI for a better society.





Abhishek completed his MBA from IIM Bangalore, a B.Tech. In Mathematics and Computing from IITGuwahati, and a PG Diploma in Cyber Law from NALSAR University, Hyderabad.











Karthik Ramasubramanian has over seven years of practice and leading Data Science and Business Analytics in Retail, FMCG, E-Commerce, Information Technology for a multi-national and two unicorn startups. A researcher and problem solver with a diverse setof experience in the data science lifecycle, starting from a data problem discovery to creating a data science prototype/product.

On the descriptive side of data science, designed, developed and spearheaded many A/B experiment frameworks for improving product features, conceptualized funnel analysis for understanding user interactions and identifying the friction points within a product, designing statistically robust metrics and visual dashboards. On the predictive side, developed intelligent chatbots which understand human-like interactions, customer segmentation models, recommendation systems, identifying medical specialization from a patient query for telemedicine, and many more.

He actively participates in analytics related thought leadership, authoring blogs & books, public speaking, meet-ups, and training & mentoring for Data Science.





 Karthik completed his M.Sc. in Theoretical Computer Science at PSG College of Technology, India, where he pioneered the application of machine learning, data mining, and fuzzy logic in his research work on the computer and network security.







Shrey Shivam extensive experience in leading the design, development, and delivery of solutions in the field of data engineering, stream analytics, machine learning, graph databases, and natural language processing. In his seven years of experience, he has worked with various conglomerates, startups, and big corporations and has gained relevant exposure to digital media, e-commerce, investment banking, insurance, and a suite of transaction-led marketplaces across music, food, lifestyle, news, legal and travel.

 

He is a keen learner and is actively engaged in designing the next generation of systems powered by artificial intelligence-based analytical and predictive models. He has taken up various roles in product management, data analytics, digital growth, system architecture, and full stack engineering. In theera of rapid acceptance and adoption of new and emerging technologies, he believes in strong technical fundamentals and advocates continuous improvement through self-learning. 

Shrey is currently leading a team of machine learning & big data engineers across the US, Europe, and India to build robust and scalable big data pipelines to implement various statistical and predictive models. Shrey has completed his BTech in Information Technology from Cochin University of Science Technology, India.