The generation of category labels affects property inferences

2006 ◽  
Author(s):  
Ben D. Jee ◽  
Jennifer Wiley
Keyword(s):  
Category Labels

scholarly journals A pilot comparison of medical records sensitivity perspectives of patients with behavioral health conditions and healthcare providers

2021 ◽  
Vol 27 (2) ◽  
pp. 146045822110099
Author(s):  
Hiral Soni ◽  
Julia Ivanova ◽  
Adela Grando ◽  
Anita Murcko ◽  
Darwyn Chern ◽  
...  
Keyword(s):  
Mental Health ◽  
Drug Abuse ◽  
Behavioral Health ◽  
Health Information ◽  
Healthcare Providers ◽  
Health Conditions ◽  
Mixed Methods Approach ◽  
Record Data ◽  
Category Labels

This pilot study compares medical record data sensitivity (e.g., depression is sensitive) and categorization perspective (e.g., depression categorized as mental health information) of patients with behavioral health conditions and healthcare providers using a mixed-methods approach employing patient’s own EHR. Perspectives of 25 English- and Spanish-speaking patients were compared with providers. Data categorization comparisons resulted in 66.3% agreements, 14.5% partial agreements, and 19.3% disagreements. Sensitivity comparisons obtained 54.5% agreement, 11.9% partial agreement, and 33.6% disagreements. Patients and providers disagreed in classification of genetic data, mental health, drug abuse, and physical health information. Factors influencing patients’ sensitivity determination were sensitive category comprehension, own experience, stigma towards category labels (e.g., drug abuse), and perception of information applicability (e.g., alcohol dependency). Knowledge of patients’ sensitivity perceptions and reconciliation with providers could expedite the development of granular and personalized consent technology.

scholarly journals A surrogate-based generic classifier for Chinese TV series reviews

2017 ◽  
Vol 45 (2) ◽  
pp. 66-74
Author(s):  
Yufeng Ma ◽  
Long Xia ◽  
Wenqi Shen ◽  
Mi Zhou ◽  
Weiguo Fan
Keyword(s):  
Design Methodology ◽  
Experimental Result ◽  
Digital Information ◽  
Data Set ◽  
Content Type ◽  
Category Labels ◽  
Information Supply Chain ◽  
Feature Selection Techniques ◽  
Practical Implications

Purpose The purpose of this paper is automatic classification of TV series reviews based on generic categories. Design/methodology/approach What the authors mainly applied is using surrogate instead of specific roles or actors’ name in reviews to make reviews more generic. Besides, feature selection techniques and different kinds of classifiers are incorporated. Findings With roles’ and actors’ names replaced by generic tags, the experimental result showed that it can generalize well to agnostic TV series as compared with reviews keeping the original names. Research limitations/implications The model presented in this paper must be built on top of an already existed knowledge base like Baidu Encyclopedia. Such database takes lots of work. Practical implications Like in digital information supply chain, if reviews are part of the information to be transported or exchanged, then the model presented in this paper can help automatically identify individual review according to different requirements and help the information sharing. Originality/value One originality is that the authors proposed the surrogate-based approach to make reviews more generic. Besides, they also built a review data set of hot Chinese TV series, which includes eight generic category labels for each review.

scholarly journals Agreeing in assessment of hyperactivity in children

2010 ◽  
Vol 63 (3-4) ◽  
pp. 249-253 ◽  
Author(s):  
Spela Golubovic ◽  
Tatjana Tubic
Keyword(s):  
Physical Activity ◽  
Standard Deviation ◽  
Rating Scale ◽  
School Age ◽  
Category Labels ◽  
The Mean ◽  
High Level ◽  
Attention Level

Introduction The study analyzes the accuracy and agreeability in evaluating hyperactivity in children. Material and methods The study sample was made of 139 children of pre-school age who participated in organized forms of physical activity. The mean age in the sample was 6.38, with a standard deviation of 1.00 years. Conner's Rating Scale was used to measure hyperactivity, and tests were also conducted to evaluate attention levels. Four independent reviewers observed each child's behavior by completing the scale. Results and discussion Eighteen children, or 13.5 percent of the sample, were identified as hyperactive in the analysis. These children also scored lower in the attention level tests. The results of the study show a correlation between the evaluators to be relatively high, bearing in mind the sources' independence. Conclusion It can be concluded that there is a mid to high-level correlation between certain reviewers' evaluations. However, even with a clearly defined view on categorizing certain behaviors as problematic, category labels still differ among evaluators.

Learning Intermediary Category Labels for Personal Recommendation

2017 ◽  
pp. 124-132
Author(s):  
Wenli Yu ◽  
Li Li ◽  
Jingyuan Wang ◽  
Dengbao Wang ◽  
Yong Wang ◽  
...  
Keyword(s):  
Personal Recommendation ◽  
Category Labels

scholarly journals Are Category Labels Primary? Children Use Similarities to Reason About Social Groups

2020 ◽  
Author(s):  
Ashley Jordan ◽  
Yarrow Dunham
Keyword(s):  
Young Children ◽  
Social Groups ◽  
Structure Study ◽  
Group Representations ◽  
Common Assumption ◽  
Low Level ◽  
Coalitional Structure ◽  
Category Labels ◽  
Types Of Information ◽  
Social Inferences

While interpersonal similarities impact young children’s peer judgments, little work has assessed whether they also guide group-based reasoning. A common assumption has been that category labels rather than “mere” similarities are unique constituents of such reasoning; the present work challenges this. Children (ages 3–9) viewed groups defined by category labels or shared preferences, and their social inferences were assessed. By age 5, children used both types of information to license predictions about preferences (Study 1, n = 129) and richer forms of coalitional structure (Study 2, n = 205). Low-level explanations could not account for this pattern (Study 3, n = 72). Finally, older but not younger children privileged labeled categories when they were pitted against similarity (Study 4, n = 51). These studies show that young children use shared preferences to reason about relationships and coalitional structure, suggesting that similarities are central to the emergence of group representations.

scholarly journals Social structure learning in human anterior insula

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Tatiana Lau ◽  
Samuel J Gershman ◽  
Mina Cikara
Keyword(s):  
Social Group ◽  
Structure Learning ◽  
Functional Neuroimaging ◽  
Group Structure ◽  
Anterior Insula ◽  
Anterior Cingulate ◽  
Latent Structure ◽  
Group Representations ◽  
Neuroimaging Data ◽  
Category Labels

Humans form social coalitions in every society, yet we know little about how we learn and represent social group boundaries. Here we derive predictions from a computational model of latent structure learning to move beyond explicit category labels and interpersonal, or dyadic, similarity as the sole inputs to social group representations. Using a model-based analysis of functional neuroimaging data, we find that separate areas correlate with dyadic similarity and latent structure learning. Trial-by-trial estimates of ‘allyship’ based on dyadic similarity between participants and each agent recruited medial prefrontal cortex/pregenual anterior cingulate (pgACC). Latent social group structure-based allyship estimates, in contrast, recruited right anterior insula (rAI). Variability in the brain signal from rAI improved prediction of variability in ally-choice behavior, whereas variability from the pgACC did not. These results provide novel insights into the psychological and neural mechanisms by which people learn to distinguish ‘us’ from ‘them.’

scholarly journals Semi-Supervised Noisy Label Learning for Chinese Medical Named Entity Recognition

2021 ◽  
pp. 1-10
Author(s):  
Zhucong Li ◽  
Zhen Gan ◽  
Baoli Zhang ◽  
Yubo Chen ◽  
Jing Wan ◽  
...  
Keyword(s):  
Hybrid System ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Final Test ◽  
Named Entity ◽  
The Core ◽  
Category Labels ◽  
Adversarial Training ◽  
Core Idea ◽  
The Impact

Abstract This paper describes our approach for the Chinese Medical named entity recognition(MER) task organized by the 2020 China conference on knowledge graph and semantic computing(CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record(EMR). We construct a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule postprocessing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we use post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.

scholarly journals Illusory essences: A bias holding back theorizing in psychological science

2020 ◽  
Author(s):  
Cameron Brick ◽  
Bruce Hood ◽  
Vebjørn Ekroll ◽  
Lee de-Wit
Keyword(s):  
Biological Psychology ◽  
Theory Development ◽  
Psychological Research ◽  
Sufficient Evidence ◽  
Complex Phenomenon ◽  
Psychological Science ◽  
Wide Range ◽  
Category Labels ◽  
Vital Forces ◽  
Holding Back

The reliance in psychology on verbal definitions means that psychological research is unusually moored to how humans think and communicate about categories. Psychological concepts (e.g., intelligence; attention) are easily assumed to represent objective, definable categories with an underlying essence. Like the 'vital forces' previously thought to animate life, these assumed essences can create an illusion of understanding. We describe a pervasive tendency across psychological science to assume that essences explain phenomena by synthesizing a wide range of research lines from cognitive, clinical, and biological psychology and neuroscience. Labeling a complex phenomenon can appear as theoretical progress before sufficient evidence that the described category has a definable essence or known boundary conditions. Category labels can further undermine progress by masking contingent and contextual relationships and obscuring the need to specify mechanisms. Finally, we highlight examples of promising methods that circumvent the lure of essences and we suggest four concrete strategies to identify and avoid essentialist intuitions in theory development.

scholarly journals Separable neural representations of sound sources: Speaker identity and musical timbre

2019 ◽  
Author(s):  
Mattson Ogg ◽  
Dustin Moraczewski ◽  
Stefanie Kuchinsky ◽  
L. Robert Slevc
Keyword(s):  
Signal To Noise Ratio ◽  
Stimulus Presentation ◽  
Machine Intelligence ◽  
Acoustic Features ◽  
Sound Sources ◽  
Temporal Lobes ◽  
Cortical Organization ◽  
Superordinate Category ◽  
Category Labels ◽  
The Many

Human listeners can quickly and easily recognize different sound sources (objects and events) in their environment. Understanding how this impressive ability is accomplished can improve signal processing and machine intelligence applications along with assistive listening technologies. However, it is not clear how the brain represents the many sounds that humans can recognize (such as speech and music) at the level of individual sources, categories and acoustic features. To examine the cortical organization of these representations, we used patterns of fMRI responses to decode 1) four individual speakers and instruments from one another (separately, within each category), 2) the superordinate category labels associated with each stimulus (speech or instrument), and 3) a set of simple synthesized sounds that could be differentiated entirely on their acoustic features. Data were collected using an interleaved silent steady state sequence to increase the temporal signal-to-noise ratio, and mitigate issues with auditory stimulus presentation in fMRI. Largely separable clusters of voxels in the temporal lobes supported the decoding of individual speakers and instruments from other stimuli in the same category. Decoding the superordinate category of each sound was more accurate and involved a larger portion of the temporal lobes. However, these clusters all overlapped with areas that could decode simple, acoustically separable stimuli. Thus, individual sound sources from different sound categories are represented in separate regions of the temporal lobes that are situated within regions implicated in more general acoustic processes. These results bridge an important gap in our understanding of cortical representations of sounds and their acoustics.

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