東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Building an enterprise chatbot = wor...

Singh, Abhishek.

FindBook

Google Book

Amazon

博客來

Building an enterprise chatbot = work with protected enterprise data using open source frameworks /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Building an enterprise chatbot/ by Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam.
其他題名:	work with protected enterprise data using open source frameworks /
作者:	Singh, Abhishek.
其他作者:	Ramasubramanian, Karthik.
出版者:	Berkeley, CA :Apress : : 2019.,
面頁冊數:	xxii, 385 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Processes in the Banking and Insurance Industry -- Chapter 2: Identifying the Sources of Data -- Chapter 3: Mining Intents from the Data Sources -- Chapter 4: Building a Business Use-Case -- Chapter 5: Natural Language Processing (NLP) -- Chapter 6: Building Chatbots Using Popular Platforms -- Chapter 7: Chatbot Platforms -- Chapter 8: Chatbot Integration Mechanism -- Chapter 9: Deployment and Continuous Improvement Framework.
Contained By:	Springer eBooks
標題:	Search engines - Programming. -
電子資源:	https://doi.org/10.1007/978-1-4842-5034-1
ISBN:	9781484250341

Building an enterprise chatbot = work with protected enterprise data using open source frameworks /
Singh, Abhishek.

Building an enterprise chatbotwork with protected enterprise data using open source frameworks /[electronic resource] :by Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam. - Berkeley, CA :Apress :2019. - xxii, 385 p. :ill., digital ;24 cm.

Chapter 1: Processes in the Banking and Insurance Industry -- Chapter 2: Identifying the Sources of Data -- Chapter 3: Mining Intents from the Data Sources -- Chapter 4: Building a Business Use-Case -- Chapter 5: Natural Language Processing (NLP) -- Chapter 6: Building Chatbots Using Popular Platforms -- Chapter 7: Chatbot Platforms -- Chapter 8: Chatbot Integration Mechanism -- Chapter 9: Deployment and Continuous Improvement Framework.

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 section, you'll discuss the importance of data transfers using natural language platforms, such as Dialogflow and LUIS, and see why this is a key process for chatbot development. In the final section, you'll work with the RASA and Botpress frameworks. By the end of Building an Enterprise Chatbot with Python, you will be able to design and develop an enterprise-ready conversational chatbot using an open source development platform to serve the end user. You will: Identify business processes 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.

ISBN: 9781484250341

Standard No.: 10.1007/978-1-4842-5034-1doiSubjects--Topical Terms:

589993
Search engines
--Programming.

LC Class. No.: QA76.76.I58 / S564 2019

Dewey Class. No.: 005.758

Building an enterprise chatbot = work with protected enterprise data using open source frameworks /
LDR:03040nmm a2200325 a 4500 001 2193391
003 DE-He213
005 20191224154452.0
006 m d
007 cr nn 008maaau
008 200514s2019 cau s 0 eng d
020 $a 9781484250341 $q (electronic bk.)
020 $a 9781484250334 $q (paper)
024 7 $a 10.1007/978-1-4842-5034-1 $2 doi
035 $a 978-1-4842-5034-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.76.I58 $b S564 2019
072 7 $a UB $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a UB $2 thema
082 0 4 $a 005.758 $2 23
090 $a QA76.76.I58 $b S617 2019
100 1 $a Singh, Abhishek. $3 901091
245 1 0 $a Building an enterprise chatbot $h [electronic resource] : $b work with protected enterprise data using open source frameworks / $c by Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a xxii, 385 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Processes in the Banking and Insurance Industry -- Chapter 2: Identifying the Sources of Data -- Chapter 3: Mining Intents from the Data Sources -- Chapter 4: Building a Business Use-Case -- Chapter 5: Natural Language Processing (NLP) -- Chapter 6: Building Chatbots Using Popular Platforms -- Chapter 7: Chatbot Platforms -- Chapter 8: Chatbot Integration Mechanism -- Chapter 9: Deployment and Continuous Improvement Framework.
520 $a 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 section, you'll discuss the importance of data transfers using natural language platforms, such as Dialogflow and LUIS, and see why this is a key process for chatbot development. In the final section, you'll work with the RASA and Botpress frameworks. By the end of Building an Enterprise Chatbot with Python, you will be able to design and develop an enterprise-ready conversational chatbot using an open source development platform to serve the end user. You will: Identify business processes 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.
650 0 $a Search engines $x Programming. $3 589993
650 0 $a Intelligent agents (Computer software) $3 567546
650 0 $a Generators (Computer programs) $3 3166399
650 0 $a Open source software. $3 581998
650 1 4 $a Computer Applications. $3 891249
650 2 4 $a Java. $3 517732
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Ramasubramanian, Karthik. $3 3219972
700 1 $a Shivam, Shrey. $3 3414531
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-5034-1
950 $a Professional and Applied Computing (Springer-12059)