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020 _a9783030042998
_9978-3-030-04299-8
024 7 _a10.1007/978-3-030-04299-8
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aGalitsky, Boris.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aDeveloping Enterprise Chatbots
_h[electronic resource] :
_bLearning Linguistic Structures /
_cby Boris Galitsky.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXV, 559 p. 198 illus., 132 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction to Chatbots -- Social Chatbots and Development Platforms -- Chatbot Components and Architectures -- Providing Natural Language Access to a Database -- Chatbot Relevance at Syntactic Level -- Semantic Skeleton-based Search for Question and Answering Chatbots -- Relevance at the Level of Paragraph: Parse Thickets -- Chatbot Thesauri -- Content Processing Pipeline -- Achieving Rhetoric Agreement in a Conversation -- Discourse-level Dialogue Management,- Chatbots Providing and Accepting Argumentation. .
520 _aA 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 unreliableand 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.
650 0 _aArtificial intelligence.
650 0 _aComputational linguistics.
650 0 _aSoftware engineering.
650 1 4 _aArtificial Intelligence.
650 2 4 _aComputational Linguistics.
650 2 4 _aSoftware Engineering.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030042981
776 0 8 _iPrinted edition:
_z9783030043001
856 4 0 _uhttps://doi.org/10.1007/978-3-030-04299-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c172866
_d172866