Delivery: Can be download immediately after purchasing. For new customer, we need process for verification from 30 mins to 12 hours.
Version: PDF/EPUB. If you need EPUB and MOBI Version, please send contact us.
Compatible Devices: Can be read on any devices.
A chatbot is expected to be capable of supporting a cohesive and coherent conversation and be knowledgeable, which makes it one of the most complex intelligent systems being designed nowadays. Designers have to learn to combine intuitive, explainable language understanding and reasoning approaches with high-performance statistical and deep learning technologies. Today, there are two popular paradigms for chatbot construction: 1. Build a bot platform with universal NLP and ML capabilities so that a bot developer for a particular enterprise, not being an expert, can populate it with training data; 2. Accumulate a huge set of training dialogue data, feed it to a deep learning network and expect the trained chatbot to automatically learn “how to chat”. Although these two approaches are reported to imitate some intelligent dialogues, both of them are unsuitable for enterprise chatbots, being unreliable and too brittle. The latter approach is based on a belief that some learning miracle will happen and a chatbot will start functioning without a thorough feature and domain engineering by an expert and interpretable dialogue management algorithms. Enterprise high-performance chatbots with extensive domain knowledge require a mix of statistical, inductive, deep machine learning and learning from the web, syntactic, semantic and discourse NLP, ontology-based reasoning and a state machine to control a dialogue. This book will provide a comprehensive source of algorithms and architectures for building chatbots for various domains based on the recent trends in computational linguistics and machine learning. The foci of this book are applications of discourse analysis in text relevant assessment, dialogue management and content generation, which help to overcome the limitations of platform-based and data driven-based approaches. Supplementary material and code is available at https://github.com/bgalitsky/relevance-based-on-parse-trees
This is a digital product.
Developing Enterprise Chatbots: Learning Linguistic Structures is written by Boris Galitsky and published by Springer. The Digital and eTextbook ISBNs for Developing Enterprise Chatbots are 9783030042998, 3030042995 and the print ISBNs are 9783030042981, 3030042987.

Great Woodcuts of Albrecht Dürer eBook
Marketing 2018, Loose-Leaf Version eBook
Forensic Psychology in Context: Nordic and International Approaches eBook
Access 2007 eBook
Using R for Modelling and Quantitative Methods in Fisheries eBook
Employee Engagement Through Effective Performance Management eBook
Gelli Plate Printing eBook
An Introduction to Programming with C++, 8th Edition eBook
New Perspectives Microsoft Office 365 & Access 2016: Comprehensive eBook
Word 2007 eBook
The Discovery of the Tomb of Tutankhamen eBook
Handbook of Scheduling eBook
Magic Capes, Amazing Powers: Transforming Superhero Play in the Classroom eBook
The Age of Surveillance Capitalism eBook
Character Design from the Ground Up eBook
MindTap Business Communication for Guffey/Seefer's Business English, 12th Edition, [Instant Access], 1 term (6 months) eBook
Disrupting Gendered Pedagogies in the Early Childhood Classroom eBook
Media Ecology eBook
The Age of Surveillance Capitalism eBook
Microsoft Office Word 2007 Step by Step eBook
Powerpoint 2007 eBook
Investments: Analysis and Management eBook
Excel 2007 eBook
Microsoft Office - Integration eBook
Microsoft Office Access 2007 Step by Step eBook
Storyboarding Essentials eBook
TIME The Science of Creativity eBook
Managerial Accounting eBook
Invitation to Public Speaking Handbook eBook
Microsoft Office Excel 2007 Step by Step eBook 


Reviews
There are no reviews yet.