A Background Knowledge Revising and Incorporating Dialogue Model

Author(s):  
Xinyan Zhao ◽  
Xiao Feng ◽  
Huanhuan Chen
Author(s):  
Ирина Владимировна Матвеева ◽  
Сергей Николаевич Саможенов ◽  
Юлия Николаевна Зинцова

Авторы публицистических текстов все чаще отходят от стандартизации речи и клишированности в пользу поиска новых экспрессивных средств выражения оценочности, которые требуют от читателя определенного количества фоновых знаний и разработанной языковой компетенции. В данной статье установлены лексические особенности использования оценочных средств в немецких публицистических текстах, выявлены их разновидности и сферы употребления. Authors of journalistic texts are increasingly moving away from the standardization of speech and cliché in favor of searching for new expressive means of expressing evaluation, which require the reader to have a certain amount of background knowledge and developed language competence. In this article, the lexical features of the use of evaluation tools in German journalistic texts are established, their varieties and areas of use are identified.


2021 ◽  
Vol 28 (2) ◽  
pp. 1017-1019
Author(s):  
Richard Wassersug

For a patient to be effective as a “patient representative” within a health-related organization, work and more than just accepting an honorific title is required. I argue that for a patient to be most effective as a patient representative requires different types of background knowledge and commitment than being a “patient advocate”. Patients need to be cautious about how, when, and where they take on an official role of either an “advocate” or “representative”, if they truly want to be a positive influence on health outcomes.


2021 ◽  
Vol 54 (4) ◽  
pp. 1-16
Author(s):  
Abdus Salam ◽  
Rolf Schwitter ◽  
Mehmet A. Orgun

This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains. These systems are very useful in such domains because they can be trained with a small amount of positive and negative examples, use declarative representations of background knowledge, and combine efficient high-level reasoning with the probability theory. The output of these systems are probabilistic rules that are easy to understand by humans, since the conditions for consequences lead to predictions that become transparent and interpretable. This survey focuses on representational approaches and system architectures, and suggests future research directions.


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