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Hauff-Hartig, S.: Automatische Transkription von Videos : Fernsehen 3.0: Automatisierte Sentimentanalyse und Zusammenstellung von Kurzvideos mit hohem Aufregungslevel KI-generierte Metadaten: Von der Technologiebeobachtung bis zum produktiven Einsatz (2021) 0.03

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Date: 22. 5.2021 12:43:05
Type: a

Holley, R.M.; Joudrey, D.N.: Aboutness and conceptual analysis : a review (2021) 0.00
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Abstract

The purpose of this paper is to provide an overview of aboutness and conceptual analysis, essential concepts for LIS practitioners to understand. Aboutness refers to the subject matter and genre/form properties of a resource. It is identified during conceptual analysis, which yields an aboutness statement, a summary of a resource's aboutness. While few scholars have discussed the aboutness determination process in detail, the methods described by Patrick Wilson, D.W. Langridge, Arlene G. Taylor, and Daniel N. Joudrey provide exemplary frameworks for determining aboutness and are presented here. Discussions of how to construct an aboutness statement and the challenges associated with aboutness determination follow.

Type

a
Fremery, W. De; Buckland, M.K.: Context, relevance, and labor (2022) 0.00
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Abstract

Since information science concerns the transmission of records, it concerns context. The transmission of documents ensures their arrival in new contexts. Documents and their copies are spread across times and places. The amount of labor required to discover and retrieve relevant documents is also formulated by context. Thus, any serious consideration of communication and of information technologies quickly leads to a concern with context, relevance, and labor. Information scientists have developed many theories of context, relevance, and labor but not a framework for organizing them and describing their relationship with one another. We propose the words context and relevance can be used to articulate a useful framework for considering the diversity of approaches to context and relevance in information science, as well as their relations with each other and with labor.

Type

a

Bi, Y.: Sentiment classification in social media data by combining triplet belief functions (2022) 0.00

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Type: a

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