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.

A Borderlands View on Latinos, Latin Americans, and Decolonization: Rethinking Mental Health
Microsoft Office Publisher 2007 Step by Step eBook
Murach's SQL Server 2016 for Developers eBook
Microsoft Office - Integration eBook
Human–Computer Interaction eBook
The Discovery of the Tomb of Tutankhamen eBook
Bring Your Own Devices (BYOD) Survival Guide eBook
Responsive Web Design with HTML5 and CSS, 5th Edition eBook
South-Western Federal Taxation 2019: Essentials of Taxation: Individuals and Business Entities eBook
Above the Fold, Revised Edition eBook
Facilitating Financial Health: Tools for Financial Planners, Coaches, and Therapists eBook
College Geometry with GeoGebra eBook
Word 2007 eBook
Network Security Essentials eBook 


Reviews
There are no reviews yet.