Training strategies and knowledge acquisition: Using the same reflective tools for different purposes

Progress in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/bfb0023916 ◽

1997 ◽

pp. 119-129 ◽

Cited By ~ 2

Author(s):

Christophe Choquet ◽

Pierre Tchounikine ◽

Francky Trichet

Keyword(s):

Knowledge Acquisition ◽

Training Strategies

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Deriving Training Strategies for Spatial Knowledge Acquisition From Behavioral, Cognitive, and Neural Foundations

Military Psychology ◽

10.1037/h0094962 ◽

2013 ◽

Vol 25 (3) ◽

pp. 191-205 ◽

Cited By ~ 2

Author(s):

Kay M. Stanney ◽

Joseph Cohn ◽

Laura Milham ◽

Kelly Hale ◽

Rudy Darken ◽

...

Keyword(s):

Knowledge Acquisition ◽

Spatial Knowledge ◽

Training Strategies ◽

Spatial Knowledge Acquisition

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Introducing Augmentative Communication: Interactive Training Strategies

NSSLHA Journal ◽

10.1044/nsshla_14_68 ◽

1986 ◽

Vol 14 ◽

pp. 68-82

Author(s):

Caroline Musselwhite

Keyword(s):

Augmentative Communication ◽

Interactive Training ◽

Training Strategies

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Supplemental Material for Brain Drain? An Examination of Stereotype Threat Effects During Training on Knowledge Acquisition and Organizational Effectiveness

Journal of Applied Psychology ◽

10.1037/apl0000171.supp ◽

2016 ◽

Keyword(s):

Knowledge Acquisition ◽

Stereotype Threat ◽

Organizational Effectiveness ◽

Brain Drain

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Part-training strategies in simulated carrier landing final approach training

PsycEXTRA Dataset ◽

10.1037/e574172012-020 ◽

1984 ◽

Author(s):

Dennis C. Wightman ◽

Gavan Lintern

Keyword(s):

Approach Training ◽

Final Approach ◽

Training Strategies

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The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

Methods of Information in Medicine ◽

10.1055/s-0038-1634568 ◽

1998 ◽

Vol 37 (04/05) ◽

pp. 327-333 ◽

Cited By ~ 3

Author(s):

F. Buekens ◽

G. De Moor ◽

A. Waagmeester ◽

W. Ceusters

Keyword(s):

Natural Language ◽

Knowledge Acquisition ◽

Natural Language Understanding ◽

Knowledge Bases ◽

Linguistic Knowledge ◽

Medical Terminology ◽

Language Understanding ◽

Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

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