Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPredicting elders' cognitive flexibility from their language use
التنبؤ بالمرونة المعرفية لشيوخ من استخدام لغتهم
592
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
جندت هذه الدراسة 51 شيويلا تتراوح أعمارهم بين 53-74 لمناقشة أنشطتهم اليومية في مجموعات التركيز.تم تحليل الخطاب المسجل باستخدام النسخة الصينية من Liwc (لين وآخرون.، 2020؛ Pennebaker et al.، 2015) للتعقيد المعرفي واللغة الديناميكية وكذلك كلمات المحتوى المتعلقة بأنشطة الشيوخ اليومية.كما تم ترميز سلوك المقاطع أثناء المحادثة وتحليلها.بعد السيطرة على التعليم والجنس والعمر، أظهرت النتائج أن أداء المرونة المعرفية يرافقه اعتماد اللغة الديناميكية والكلمات البصيرة والكلمات العائلية.تعمل هذه النتائج كأساس للتنبؤ بالمرونة المعرفية لشيوخ من خلال استخدامها اليومي.
This study recruited 51 elders aged 53-74 to discuss their daily activities in focus groups. The transcribed discourse was analyzed using the Chinese version of LIWC (Lin et al., 2020; Pennebaker et al., 2015) for cognitive complexity and dynamic language as well as content words related to elders' daily activities. The interruption behavior during the conversation was also coded and analyzed. After controlling for education, gender and age, the results showed that cognitive flexibility performance was accompanied by the increasing adoption of dynamic language, insight words and family words. These findings serve as the basis for predicting elders' cognitive flexibility through their daily language use.
References used
https://aclanthology.org/
rate research
Read More
As the average life expectancy of Chinese people rises, the health care problems of the elderly are becoming more diverse, and the demand for long-term care is also increasing. Therefore, how to help the elderly have a good quality of life and mainta
in their dignity is what we need to think about. This research intends to explore the characteristics of natural language of normal aging people through a deep model. First, we collect information through focus groups so that the elders can naturally interact with other participants in the process. Then, through the word vector model and regression model, an executive function prediction model based on dialogue data is established to help understand the degradation trajectory of executive function and establish an early warning.
Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summar
izers. In particular, we generate unlabeled RST-style discourse trees from the self-attention matrices of the transformer model. Experiments across models and datasets reveal that the summarizer learns both, dependency- and constituency-style discourse information, which is typically encoded in a single head, covering long- and short-distance discourse dependencies. Overall, the experimental results suggest that the learned discourse information is general and transferable inter-domain.
In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Subst
ance consumption, and Frightening scenes. The problem is handled using a siamese network-based multitask framework which concurrently improves the interpretability of the predictions. The experimental results show that our method outperforms the previous state-of-the-art model and provides useful information to interpret model predictions. The proposed dataset and source code are publicly available at our GitHub repository.
Abstract We adopt an evolutionary view on language change in which cognitive factors (in addition to social ones) affect the fitness of words and their success in the linguistic ecosystem. Specifically, we propose a variety of psycholinguistic factor
s---semantic, distributional, and phonological---that we hypothesize are predictive of lexical decline, in which words greatly decrease in frequency over time. Using historical data across three languages (English, French, and German), we find that most of our proposed factors show a significant difference in the expected direction between each curated set of declining words and their matched stable words. Moreover, logistic regression analyses show that semantic and distributional factors are significant in predicting declining words. Further diachronic analysis reveals that declining words tend to decrease in the diversity of their lexical contexts over time, gradually narrowing their ecological niches'.
Flight delays are frequent all over the world (about 20% of airline flights arrive more than 15 minutes
late) and they are estimated to have an annual cost of several tens of billion dollars. This scenario makes
the prediction of flight delays a pr
imary issue for airlines and travelers. The main goal of this work is to
implement a predictor of the arrival delay of a scheduled flight due to weather conditions. The predicted
arrival delay takes into consideration both flight information (origin airport, destination airport, scheduled
departure and arrival time) and weather conditions at origin airport and destination airport according to
the flight timetable. Airline flights and weather observations datasets have been analyzed and mined using
parallel algorithms implemented as MapReduce programs executed on a Cloud platform. The results show
a high accuracy in predicting delays above a given threshold. For instance, with a delay threshold of 15
minutes we achieve an accuracy of 74.2% and 71.8% recall on delayed flights, while with a threshold of
60 minutes the accuracy is 85.8% and the delay recall is 86.9%. Furthermore, the experimental results
demonstrate the predictor scalability that can be achieved performing data preparation and mining tasks
as MapReduce applications on the Cloud.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
