國立高雄大學圖資館 |

登入

回首頁

Neural approaches to conversational information retrieval

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Neural approaches to conversational information retrievalby Jianfeng Gao ... [et al.].
其他作者:	Gao, Jianfeng.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xii, 211 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computational linguistics.
電子資源:	https://doi.org/10.1007/978-3-031-23080-6
ISBN:	9783031230806$q(electronic bk.)

Neural approaches to conversational information retrieval
Neural approaches to conversational information retrieval[electronic resource] /by Jianfeng Gao ... [et al.]. - Cham :Springer International Publishing :2023. - xii, 211 p. :ill., digital ;24 cm. - The information retrieval series,v. 442730-6836 ;. - Information retrieval series ;v.37..

This book surveys recent advances in Conversational Information Retrieval (CIR), focusing on neural approaches that have been developed in the last few years. Progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI, leading to a plethora of commercial conversational services that allow naturally spoken and typed interaction, increasing the need for more human-centric interactions in IR. The book contains nine chapters. Chapter 1 motivates the research of CIR by reviewing the studies on how people search and subsequently defines a CIR system and a reference architecture which is described in detail in the rest of the book. Chapter 2 provides a detailed discussion of techniques for evaluating a CIR system - a goal-oriented conversational AI system with a human in the loop. Then Chapters 3 to 7 describe the algorithms and methods for developing the main CIR modules (or sub-systems) In Chapter 3, conversational document search is discussed, which can be viewed as a sub-system of the CIR system. Chapter 4 is about algorithms and methods for query-focused multi-document summarization. Chapter 5 describes various neural models for conversational machine comprehension, which generate a direct answer to a user query based on retrieved query-relevant documents, while Chapter 6 details neural approaches to conversational question answering over knowledge bases, which is fundamental to the knowledge base search module of a CIR system. Chapter 7 elaborates various techniques and models that aim to equip a CIR system with the capability of proactively leading a human-machine conversation. Chapter 8 reviews a variety of commercial systems for CIR and related tasks. It first presents an overview of research platforms and toolkits which enable scientists and practitioners to build conversational experiences, and continues with historical highlights and recent trends in a range of application areas. Chapter 9 eventually concludes the book with a brief discussion of research trends and areas for future work. The primary target audience of the book are the IR and NLP research communities. However, audiences with another background, such as machine learning or human-computer interaction, will also find it an accessible introduction to CIR.

ISBN: 9783031230806$q(electronic bk.)

Standard No.: 10.1007/978-3-031-23080-6doiSubjects--Topical Terms:

181250
Computational linguistics.

LC Class. No.: P98

Dewey Class. No.: 006.35

Neural approaches to conversational information retrieval
LDR:03399nmm a2200349 a 4500 001 642479
003 DE-He213
005 20230316233245.0
006 m d
007 cr nn 008maaau
008 231123s2023 sz s 0 eng d
020 $a 9783031230806$q(electronic bk.)
020 $a 9783031230790$q(paper)
024 7 $a 10.1007/978-3-031-23080-6 $2 doi
035 $a 978-3-031-23080-6
040 $a GP $c GP
041 0 $a eng
050 4 $a P98
072 7 $a UNH $2 bicssc
072 7 $a UND $2 bicssc
072 7 $a COM030000 $2 bisacsh
072 7 $a UNH $2 thema
072 7 $a UND $2 thema
082 0 4 $a 006.35 $2 23
090 $a P98 $b .N494 2023
245 0 0 $a Neural approaches to conversational information retrieval $h [electronic resource] / $c by Jianfeng Gao ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xii, 211 p. : $b ill., digital ; $c 24 cm.
490 1 $a The information retrieval series, $x 2730-6836 ; $v v. 44
520 $a This book surveys recent advances in Conversational Information Retrieval (CIR), focusing on neural approaches that have been developed in the last few years. Progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI, leading to a plethora of commercial conversational services that allow naturally spoken and typed interaction, increasing the need for more human-centric interactions in IR. The book contains nine chapters. Chapter 1 motivates the research of CIR by reviewing the studies on how people search and subsequently defines a CIR system and a reference architecture which is described in detail in the rest of the book. Chapter 2 provides a detailed discussion of techniques for evaluating a CIR system - a goal-oriented conversational AI system with a human in the loop. Then Chapters 3 to 7 describe the algorithms and methods for developing the main CIR modules (or sub-systems) In Chapter 3, conversational document search is discussed, which can be viewed as a sub-system of the CIR system. Chapter 4 is about algorithms and methods for query-focused multi-document summarization. Chapter 5 describes various neural models for conversational machine comprehension, which generate a direct answer to a user query based on retrieved query-relevant documents, while Chapter 6 details neural approaches to conversational question answering over knowledge bases, which is fundamental to the knowledge base search module of a CIR system. Chapter 7 elaborates various techniques and models that aim to equip a CIR system with the capability of proactively leading a human-machine conversation. Chapter 8 reviews a variety of commercial systems for CIR and related tasks. It first presents an overview of research platforms and toolkits which enable scientists and practitioners to build conversational experiences, and continues with historical highlights and recent trends in a range of application areas. Chapter 9 eventually concludes the book with a brief discussion of research trends and areas for future work. The primary target audience of the book are the IR and NLP research communities. However, audiences with another background, such as machine learning or human-computer interaction, will also find it an accessible introduction to CIR.
650 0 $a Computational linguistics. $3 181250
650 0 $a Information retrieval. $3 215224
650 0 $a Human-computer interaction. $3 189333
650 1 4 $a Information Storage and Retrieval. $3 274190
650 2 4 $a User Interfaces and Human Computer Interaction. $3 274517
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Gao, Jianfeng. $3 949832
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Information retrieval series ; $v v.37. $3 775332
856 4 0 $u https://doi.org/10.1007/978-3-031-23080-6
950 $a Computer Science (SpringerNature-11645)