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3 books on Data Annotation [PDF]
Updated: May 19, 2024 | 25 |
Books on Data Annotation serve as invaluable resources for startups specializing in ML data labeling and annotation. They offer a wealth of knowledge, techniques, and best practices to develop robust and accurate labeled datasets, which are the backbone of machine learning models. These resources delve into the intricacies of data labeling, covering topics like annotation tools, labeling guidelines, quality control, and domain-specific challenges. By studying these books, startups can enhance the consistency and reliability of their labeled datasets, crucial for training high-performing machine learning algorithms. Additionally, these books often discuss emerging trends and evolving industry standards, enabling startups to stay at the forefront of the data annotation field and provide innovative solutions to meet the diverse needs of their clients.
1. Handbook of Linguistic Annotation
2017 by Nancy Ide, James Pustejovsky
The "Handbook of Linguistic Annotation" presents a comprehensive examination of the science behind linguistic annotation. Esteemed experts in the field serve as guides, leading readers through the intricate process of modeling, creating annotation languages, constructing corpora, and rigorously assessing their correctness. This indispensable resource caters to both computer scientists and linguistic researchers, given the increasing significance of linguistic annotation in computational linguistics and its pivotal role in shaping language models for natural language processing applications. The first section of the book encompasses all stages of the linguistic annotation journey, spanning from the design of annotation schemes and selection of representation formats to both manual and automated annotation procedures, evaluation techniques, and the continuous enhancement of annotation precision. The second section features insightful case studies that span a wide spectrum of linguistic annotation types, encompassing aspects like morpho-syntactic tagging, syntactic analyses, diverse semantic analyses (including semantic roles, named entities, sentiment, and opinion), as well as temporal, spatial, and discourse-level analyses such as discourse structure and co-reference resolution. Each case study intricately explores the phases and processes expounded upon in the preceding chapters of the first section, offering a comprehensive view of the field.
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2. Deep Learning and Data Labeling for Medical Applications
2016 by Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Julien Cornebise
This book encompasses the peer-reviewed proceedings of two workshops that took place during the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. The first workshop, titled "Large-Scale Annotation of Biomedical Data and Expert Label Synthesis" (LABELS 2016), and the second, the "International Workshop on Deep Learning in Medical Image Analysis" (DLMIA 2016), contributed to this compilation. Among the 52 submissions received, a careful review process resulted in the selection of 28 revised regular papers featured in this volume. Within LABELS 2016, the 7 chosen papers explore themes such as crowd-sourcing methods, active learning, transfer learning, semi-supervised learning, and modeling of label uncertainty. In the case of DLMIA 2016, the selection comprises 21 papers spanning a diverse range of subjects, including image description, medical diagnosis based on imaging and signals, medical image reconstruction, model selection using deep learning, meta-heuristic techniques for fine-tuning parameters in deep learning architectures, and various applications harnessing deep learning methodologies.
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3. Provenance and Annotation of Data and Processes
2008 by Juliana Freire, David Koop
This volume comprises the meticulously reviewed post-conference proceedings stemming from the Second International Provenance and Annotation Workshop, IPAW 2008, which took place in Salt Lake City, UT, USA, during June 2007. Among the 40 submitted papers, 14 full papers and 15 short and demo papers were selected for inclusion, along with two keynote lectures. These contributions are thoughtfully organized into thematic sections, covering various aspects of provenance, including models and querying, visualization and addressing failures in provenance, identity-related considerations, the intersection of provenance and workflows, and its applications in contexts like data streams and collaboration.
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How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Handbook of Linguistic Annotation
2017 by Nancy Ide, James Pustejovsky
The "Handbook of Linguistic Annotation" presents a comprehensive examination of the science behind linguistic annotation. Esteemed experts in the field serve as guides, leading readers through the intricate process of modeling, creating annotation languages, constructing corpora, and rigorously assessing their correctness. This indispensable resource caters to both computer scientists and linguistic researchers, given the increasing significance of linguistic annotation in computational linguistics and its pivotal role in shaping language models for natural language processing applications. The first section of the book encompasses all stages of the linguistic annotation journey, spanning from the design of annotation schemes and selection of representation formats to both manual and automated annotation procedures, evaluation techniques, and the continuous enhancement of annotation precision. The second section features insightful case studies that span a wide spectrum of linguistic annotation types, encompassing aspects like morpho-syntactic tagging, syntactic analyses, diverse semantic analyses (including semantic roles, named entities, sentiment, and opinion), as well as temporal, spatial, and discourse-level analyses such as discourse structure and co-reference resolution. Each case study intricately explores the phases and processes expounded upon in the preceding chapters of the first section, offering a comprehensive view of the field.
Download PDF
2. Deep Learning and Data Labeling for Medical Applications
2016 by Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Julien Cornebise
This book encompasses the peer-reviewed proceedings of two workshops that took place during the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. The first workshop, titled "Large-Scale Annotation of Biomedical Data and Expert Label Synthesis" (LABELS 2016), and the second, the "International Workshop on Deep Learning in Medical Image Analysis" (DLMIA 2016), contributed to this compilation. Among the 52 submissions received, a careful review process resulted in the selection of 28 revised regular papers featured in this volume. Within LABELS 2016, the 7 chosen papers explore themes such as crowd-sourcing methods, active learning, transfer learning, semi-supervised learning, and modeling of label uncertainty. In the case of DLMIA 2016, the selection comprises 21 papers spanning a diverse range of subjects, including image description, medical diagnosis based on imaging and signals, medical image reconstruction, model selection using deep learning, meta-heuristic techniques for fine-tuning parameters in deep learning architectures, and various applications harnessing deep learning methodologies.
Download PDF
3. Provenance and Annotation of Data and Processes
2008 by Juliana Freire, David Koop
This volume comprises the meticulously reviewed post-conference proceedings stemming from the Second International Provenance and Annotation Workshop, IPAW 2008, which took place in Salt Lake City, UT, USA, during June 2007. Among the 40 submitted papers, 14 full papers and 15 short and demo papers were selected for inclusion, along with two keynote lectures. These contributions are thoughtfully organized into thematic sections, covering various aspects of provenance, including models and querying, visualization and addressing failures in provenance, identity-related considerations, the intersection of provenance and workflows, and its applications in contexts like data streams and collaboration.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
