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To demonstrate the usage of the framework and the reference test collection, we present a specific evaluation study on CAFÉ, an adaptive filtering engine designed for supporting task-based information exploration.
This means that changes demonstrated for well supported tasks are more likely to reflect real changes in the underlying processes than they are likely to happen by chance.
In the context of software development, only few techniques exist for supporting these tasks.
Recording search histories, presenting them to the searcher, and building additional interface tools on them offer many opportunities for supporting user tasks in information seeking and use.
Given the usefulness of ontologies for that purpose, anything that might assist semantic Web annotation and ontology learning is a valuable resource, and Relation Extraction is an advantageous means for supporting those tasks [93].
Yet, they still challenge developers by their complexity: Apart from a sound knowledge base they likewise require comprehensive user interface (UI) and interaction design for supporting the task at hand optimally.
Todoist for iOS now also supports task search and deep linking in Spotlight.
While I don't collaborate with other people on projects using Todoist, the service has supported task comments for a while.
The selected NLP tools for our work, with minor extensions and customization done, have proven adequate for supporting the NLP tasks of our work.
In this sense, knowledge management technologies have proved to be highly promising for supporting this integration task.
A standalone research database is not suitable for supporting this difficult task.
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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