TY  - BOOK
AU  - Tsai,Chen-Tse
AU  - Upadhyay,Shyam
AU  - Roth,Dan
ED  - SpringerLink (Online service)
TI  - Multilingual Entity Linking
T2  - Synthesis Lectures on Human Language Technologies,
SN  - 9783031749018
AV  - QA76.9.N38 
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Natural language processing (Computer science)
KW  - Expert systems (Computer science)
KW  - Machine learning
KW  - Data mining
KW  - Image processing
KW  - Digital techniques
KW  - Computer vision
KW  - Artificial intelligence
KW  - Natural Language Processing (NLP)
KW  - Knowledge Based Systems
KW  - Machine Learning
KW  - Data Mining and Knowledge Discovery
KW  - Computer Imaging, Vision, Pattern Recognition and Graphics
KW  - Artificial Intelligence
N1  - Chapter 1 Introduction to Entity Discovery and Linking -- Chapter 2 Knowledge Bases, Datasets, and Evaluation -- Chapter 3 Overview of Entity Discovery and Linking Pipeline
N2  - This book focuses on Entity Discovery and Linking (EDL), which is the problem of identifying concepts and entities, disambiguating them, and grounding them to one or more knowledge bases (KBs). The authors first provide background on the topic and emphasize why it is a crucial step toward understanding natural language text. As most of the content on the internet is not in English, the book also discusses cross-lingual EDL. The authors present the challenges associated with EDL problems and explain the existing solutions. The book covers the core challenges that apply to all EDL problems, as well as the additional challenges associated with cross-lingual EDL problems. The authors also survey relevant research papers, highlight recent trends, and identify areas for future research. Introduces entity discovery and linking and discusses its important role in natural language processing Explains the challenges and existing solutions to both monolingual and cross-lingual EDL problems Identifies resources for readers, including popular datasets, knowledge bases, and evaluation methodologies
UR  - https://doi.org/10.1007/978-3-031-74901-8
ER  -