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This paper investigates the ways in which people group or categorize documents associated with a voluminous work to guide the construction of organized displays for information retrieval systems (IRSs).
Drawing on the success of Web 2.0 and theories from knowledge management, we argue that a shared document repository with no central organizer may benefit from collective taxonomizing: allowing community members to categorize documents with local document hierarchies and systematically coalesce those local hierarchies into a global taxonomy.
Within each type, we further categorize documents according to their structure, predominant characteristics, and content areas, with the indicator documents being divided into two sub-categories: (i) early learning and development standards documents; and (ii) skills progression documents, or "maps".
She estimated that on that case alone, she was able to consolidate documents and automate extraction of content and metadata (such as author and date), identify document language and categorize documents in a variety of ways (by function, source, etc.).
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As we itemize, categorize, document and double check, it's all about dollars and cents, and you can't even watch an episode of your favorite show without an exuberant accountant yelling at you to get your billions back, America!
One of them consists in categorizing documents regarding different criteria (allele, expression, gene ontology annotation, and tumor) in the genomics domain.
He emphasizes the need for businesses to own their data and information in a deep way i.e. have a solid understanding of where data is located, how data is categorized (documenting in a menu or guidebook helps), and refine/create a system for how data has and will be sorted in the future.
These observations were then categorized, documented and frequency of each factor was determined.
After obtaining probability estimates using fold-in, to categorize new documents to a specific category, similarity scores between new documents and documents in the training set are computed.
Specifically, we categorize the documents reviewed in this study into two types: "indicator documents" and "framework documents".
Why it matters: This may become a high-profile example of a kind of white-collar automation that has been under way for some time: the use of software to review, discover, and categorize legal documents.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com