The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform
Sally Bryant - One of the best experts on this subject based on the ideXlab platform.
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InfoGuides: Film 200 (Professor Christina Sibul): Home
2016Co-Authors: Sally BryantAbstract:An introductory study of the critical and Technical Language associated with the Film Studies discipline.
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InfoGuides: Film 200 (Professor Christina Sibul): MLA Citation Style 8th edition
2016Co-Authors: Sally BryantAbstract:An introductory study of the critical and Technical Language associated with the Film Studies discipline.
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InfoGuides: Film 200 (Professor Christina Sibul): Setting up Google Scholar
2016Co-Authors: Sally BryantAbstract:An introductory study of the critical and Technical Language associated with the Film Studies discipline.
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InfoGuides: Film 200 (Professor Christina Sibul): Other Resources
2016Co-Authors: Sally BryantAbstract:An introductory study of the critical and Technical Language associated with the Film Studies discipline.
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InfoGuides: Film 200 (Professor Joi Carr): Other Resources
2010Co-Authors: Sally BryantAbstract:An introductory study of the critical and Technical Language associated with the Film Studies discipline.
Sarah Lukens - One of the best experts on this subject based on the ideXlab platform.
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Technical Language processing: Unlocking maintenance knowledge
Manufacturing Letters, 2021Co-Authors: Michael P. Brundage, Thurston Sexton, Melinda Hodkiewicz, Alden A. Dima, Sarah LukensAbstract:Abstract Out-of-the-box natural-Language processing (NLP) pipelines need re-imagining to understand and meet the requirements of engineering data. Text-based documents account for a significant portion of data collected during the life cycle of asset management and the valuable information these documents contain is underutilized in analysis. Meanwhile, researchers historically design NLP pipelines with non-Technical Language in mind. This means industrial implementations are built on tools intended for non-Technical use cases, suffering from a lack of verification, validation, and ultimately, personnel trust. To mitigate these sources of risk, we encourage a holistic, domain-driven approach to using NLP in a Technical engineering setting, a paradigm we refer to as Technical Language Processing (TLP). Toward this end, the industrial asset management community must collectively redouble efforts toward production of and consensus around key domain-specific resources, including: (1) goal-driven data representations, (2) flexible entity type definitions and dictionaries, and (3) improved access to data-sets – raw and annotated. This collective action allows the maintenance community to follow in the path of other scientific communities, e.g., medicine, to develop and utilize these public resources to make TLP a key contributor to Industry 4.0.
Jan Smedslund - One of the best experts on this subject based on the ideXlab platform.
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Psychologic: A Technical Language for Psychology
Psychological Inquiry, 1991Co-Authors: Jan SmedslundAbstract:It has been an intensely stimulating experience to read the commentaries, and I am grateful for the opportunity to engage in dialogue. I have organized my reply as follows: I begin by giving a brief summary of how psychologic (PL) developed. Then, I expand on four themes that are directly or indirectly referred to in most of the commentaries. These themes are (a) the intended uses of PL, (b) its implications for empirical research, (c) its universality, and (d) the issue of classical versus prototype views on concepts. Finally, I respond to some of the individual comments not subsumable under the four general themes.
Michael P. Brundage - One of the best experts on this subject based on the ideXlab platform.
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Technical Language processing: Unlocking maintenance knowledge
Manufacturing Letters, 2021Co-Authors: Michael P. Brundage, Thurston Sexton, Melinda Hodkiewicz, Alden A. Dima, Sarah LukensAbstract:Abstract Out-of-the-box natural-Language processing (NLP) pipelines need re-imagining to understand and meet the requirements of engineering data. Text-based documents account for a significant portion of data collected during the life cycle of asset management and the valuable information these documents contain is underutilized in analysis. Meanwhile, researchers historically design NLP pipelines with non-Technical Language in mind. This means industrial implementations are built on tools intended for non-Technical use cases, suffering from a lack of verification, validation, and ultimately, personnel trust. To mitigate these sources of risk, we encourage a holistic, domain-driven approach to using NLP in a Technical engineering setting, a paradigm we refer to as Technical Language Processing (TLP). Toward this end, the industrial asset management community must collectively redouble efforts toward production of and consensus around key domain-specific resources, including: (1) goal-driven data representations, (2) flexible entity type definitions and dictionaries, and (3) improved access to data-sets – raw and annotated. This collective action allows the maintenance community to follow in the path of other scientific communities, e.g., medicine, to develop and utilize these public resources to make TLP a key contributor to Industry 4.0.
Linda M.g. Vancleef - One of the best experts on this subject based on the ideXlab platform.
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Effects of (in)validation and plain versus Technical Language on the experience of experimentally induced pain : A computer controlled simulation paradigm
Journal of behavior therapy and experimental psychiatry, 2019Co-Authors: Martina D'agostini, Kai Karos, Hanne P.j. Kindermans, Linda M.g. VancleefAbstract:Abstract Background and objectives Amongst social contextual influences on pain, the manner in which pain and painful procedures are communicated to patients is considered an important contributor to the subjective experience of pain. Threatening information, e.g., by the use of Technical Language, is suggested to increase pain reports. Validation, or communicating understanding towards another person reporting personal experiences, is suggested to reduce pain. The current study examines effects of both information Language (Technical vs. plain Language) and validation (validation vs. invalidation) on the subjective experience of experimentally induced pain. Methods Pain-free participants (N = 132) were randomly assigned to one of four groups as formed by manipulations of validation and information Language. After reading a description concerning the upcoming thermal stimulus formulated in Technical or plain Language, participants engaged in a computer controlled simulation (CCS; based on virtual reality technology). Participants received three thermal stimuli while interacting with an avatar who either validated or invalidated their experience during the CCS. Pain intensity and pain unpleasantness were assessed after each stimulus. Results The validation manipulation showed to be effective, but the information Language manipulation did not induce differential threat expectancies. Results show no effect of validation or information Language on subjective pain reports. Limitations Suboptimality of the information Language manipulation and shortcomings of the CCS procedure might account for current findings. Conclusions The study offers an interesting model for the further experimental study of isolated and combined effects of (social) contextual factors on pain. Diverse future research avenues are discussed.
