Stock photo
Stock photo
Synthesis Lectures on Human Language Technologies Ser.: Pretrained Transformers for Text Ranking : BERT and Beyond by Andrew Yates, Jimmy Lin and Rodrigo Nogueira (2021, Trade Paperback)
B
Bookbarn87 (4096)
99.6% positive feedback
Price:
$34.00
Free shipping
Returns:
30 days returns. Buyer pays for return shipping. If you use an eBay shipping label, it will be deducted from your refund amount.
Condition:
EAN : 9781636392288. First Edition : False. Pages : 325. About Suffolk Books. Condition : Very Good.
- Buy It NowPretrained Transformers for Text Ranking: Bert and Beyond (Synthesis Lectures on
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
About this product
Product Identifiers
PublisherMorgan & Claypool Publishers
ISBN-101636392288
ISBN-139781636392288
eBay Product ID (ePID)12057263198
Product Key Features
Number of Pages325 Pages
Publication NamePretrained Transformers for Text Ranking : Bert and Beyond
LanguageEnglish
Publication Year2021
SubjectMechanical, General, Applied
TypeTextbook
Subject AreaMathematics, Technology & Engineering
AuthorAndrew Yates, Jimmy Lin, Rodrigo Nogueira
SeriesSynthesis Lectures on Human Language Technologies Ser.
FormatTrade Paperback
Dimensions
Item Height0.7 in
Item Weight19.9 Oz
Item Length9.2 in
Item Width7.5 in
Additional Product Features
Intended AudienceScholarly & Professional
Table Of ContentPreface Acknowledgments Introduction Setting the Stage Multi-Stage Architectures for Reranking Refining Query and Document Representations Learned Dense Representations for Ranking Future Directions and Conclusions Bibliography Authors' Biographies
SynopsisProvides a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area., The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing (NLP) applications . This book provides an overview of text ranking with neural network architectures known as transformers, of which BERT (Bidirectional Encoder Representations from Transformers) is the best-known example. The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in NLP, information retrieval (IR), and beyond. This book provides a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. It covers a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage architectures and dense retrieval techniques that perform ranking directly. Two themes pervade the book: techniques for handling long documents, beyond typical sentence-by-sentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i.e., result quality) and efficiency (e.g., query latency, model and index size). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this book also attempts to prognosticate where the field is heading.
Be the first to write a review
