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Developing Enterprise Chatbots - Learning Linguistic Structures (Hardcover, 1st ed. 2019)
Loot Price: R2,324
Discovery Miles 23 240
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Developing Enterprise Chatbots - Learning Linguistic Structures (Hardcover, 1st ed. 2019)
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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
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