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3 books on Data Annotation [PDF]
October 24, 2024 | 25 |
Books on Data Annotation describe techniques and best practices to develop robust and accurate labeled datasets to feed machine learning models. They cover topics like annotation tools, labeling guidelines, quality control, and domain-specific challenges.
1. Handbook of Linguistic Annotation
2017 by Nancy Ide, James Pustejovsky
Imagine taking the humble act of speaking and turning it into a full-blown galactic adventure where words are dissected, tagged and meticulously cataloged like rare interplanetary specimens. The Handbook of Linguistic Annotation is your trusty guide through this vast linguistic cosmos. It dives headfirst into the world of annotation, where brave linguists and computer scientists battle through morpho-syntactic jungles, semantic wormholes and the treacherous asteroid fields of discourse analysis. You’ll explore how annotation schemes are designed (a bit like planning a spaceship route, but with punctuation), how corpora are constructed (no, not that kind) and how every single tag is evaluated with the seriousness of a galactic council deciding the fate of the universe. And for those who prefer a hands-on approach, there are case studies galore, from teaching machines about sentiment (apparently, they have feelings now) to resolving the mystery of co-references, which is just a fancy way of saying “Who said what?” It’s the ultimate guide for those curious about how to teach computers to understand the oddities of human communication without causing a cosmic misunderstanding.
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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
Ah, the wonders of deep learning! Or, as we like to call it, teaching computers how to look at squiggly lines and say, “Aha! That’s a spleen.” Deep Learning and Data Labeling for Medical Applications is a rollicking tour through the ever-expanding cosmos of medical imaging, complete with a pair of workshops that sound like they could either be about cutting-edge technology or obscure interstellar councils. From Athens (the one on Earth) in 2016, this collection of papers delves into everything from crowd-sourcing expert opinions to using meta-heuristics (which sounds suspiciously magical) to fine-tune algorithms. If you’ve ever dreamed of a universe where algorithms are doctors and radiology involves more programming than peering at X-rays, then this is your golden ticket.
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3. Provenance and Annotation of Data and Processes
2008 by Juliana Freire, David Koop
In the grand tradition of tracking down who did what, when and why (and preferably blaming someone else), we have Provenance and Annotation of Data and Processes. It’s not a detective novel, but it does involve a lot of sleuthing—mainly to figure out how data and processes leave behind evidence of their existence like breadcrumbs for very clever birds (or computer scientists). Set against the backdrop of Salt Lake City in 2007 (no dramatic explosions, but plenty of post-conference caffeine), the book offers a thrilling lineup of papers on subjects like identity (no, not yours), visualizing data trails and fixing things when the universe inevitably hiccups. In short, it’s the kind of adventure for anyone who’s ever found the phrase “data streams and collaboration” more thrilling than a sci-fi blockbuster.
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
1. Handbook of Linguistic Annotation
2017 by Nancy Ide, James Pustejovsky
Imagine taking the humble act of speaking and turning it into a full-blown galactic adventure where words are dissected, tagged and meticulously cataloged like rare interplanetary specimens. The Handbook of Linguistic Annotation is your trusty guide through this vast linguistic cosmos. It dives headfirst into the world of annotation, where brave linguists and computer scientists battle through morpho-syntactic jungles, semantic wormholes and the treacherous asteroid fields of discourse analysis. You’ll explore how annotation schemes are designed (a bit like planning a spaceship route, but with punctuation), how corpora are constructed (no, not that kind) and how every single tag is evaluated with the seriousness of a galactic council deciding the fate of the universe. And for those who prefer a hands-on approach, there are case studies galore, from teaching machines about sentiment (apparently, they have feelings now) to resolving the mystery of co-references, which is just a fancy way of saying “Who said what?” It’s the ultimate guide for those curious about how to teach computers to understand the oddities of human communication without causing a cosmic misunderstanding.
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
Ah, the wonders of deep learning! Or, as we like to call it, teaching computers how to look at squiggly lines and say, “Aha! That’s a spleen.” Deep Learning and Data Labeling for Medical Applications is a rollicking tour through the ever-expanding cosmos of medical imaging, complete with a pair of workshops that sound like they could either be about cutting-edge technology or obscure interstellar councils. From Athens (the one on Earth) in 2016, this collection of papers delves into everything from crowd-sourcing expert opinions to using meta-heuristics (which sounds suspiciously magical) to fine-tune algorithms. If you’ve ever dreamed of a universe where algorithms are doctors and radiology involves more programming than peering at X-rays, then this is your golden ticket.
Download PDF
3. Provenance and Annotation of Data and Processes
2008 by Juliana Freire, David Koop
In the grand tradition of tracking down who did what, when and why (and preferably blaming someone else), we have Provenance and Annotation of Data and Processes. It’s not a detective novel, but it does involve a lot of sleuthing—mainly to figure out how data and processes leave behind evidence of their existence like breadcrumbs for very clever birds (or computer scientists). Set against the backdrop of Salt Lake City in 2007 (no dramatic explosions, but plenty of post-conference caffeine), the book offers a thrilling lineup of papers on subjects like identity (no, not yours), visualizing data trails and fixing things when the universe inevitably hiccups. In short, it’s the kind of adventure for anyone who’s ever found the phrase “data streams and collaboration” more thrilling than a sci-fi blockbuster.
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
