Transductive Learning from Textual Data with Relevant Example Selection

2010 ◽  
pp. 470-484
Author(s):  
Michelangelo Ceci
Keyword(s):  
Transductive Learning ◽  
Textual Data

Modeling longitudinal dynamics in textual data

2003 ◽  
Author(s):  
Kevin Dooley ◽  
Steven Corman
Keyword(s):  
Longitudinal Dynamics ◽  
Textual Data

Patients’ Perceptions of Caring and Uncaring Nursing Encounters in Inpatient Rehabilitation Settings

2019 ◽  
Vol 21 (2) ◽  
Author(s):  
Joan C Cheruiyot ◽  
Petra Brysiewicz
Keyword(s):  
South Africa ◽  
Content Analysis ◽  
Inpatient Rehabilitation ◽  
Structured Interview ◽  
The Self ◽  
Personal Relationships ◽  
Individual Interviews ◽  
Textual Data ◽  
Interview Guide ◽  
Being There

This study explores and describes caring and uncaring nursing encounters from the perspective of the patients admitted to inpatient rehabilitation settings in South Africa. The researchers used an exploratory descriptive design. A semi-structured interview guide was used to collect data through individual interviews with 17 rehabilitation patients. Content analysis allowed for the analysis of textual data. Five categories of nursing encounters emerged from the analysis: noticing and acting, and being there for you emerged as categories of caring nursing encounters, and being ignored, being a burden, and deliberate punishment emerged as categories of uncaring nursing encounters. Caring nursing encounters make patients feel important and that they are not alone in the rehabilitation journey, while uncaring nursing encounters makes the patients feel unimportant and troublesome to the nurses. Caring nursing encounters give nurses an opportunity to notice and acknowledge the existence of vulnerability in the patients and encourage them to be present at that moment, leading to empowerment. Uncaring nursing encounters result in patients feeling devalued and depersonalised, leading to discouragement. It is recommended that nurses strive to develop personal relationships that promote successful nursing encounters. Further, nurses must strive to minimise the patients’ feelings of guilt and suffering, and to make use of tools, for example the self-perceived scale, to measure this. Nurses must also perform role plays on how to handle difficult patients such as confused, demanding and rude patients in the rehabilitation settings.

Query expansion with the minimum user feedback by transductive learning

2005 ◽  
Author(s):  
Masayuki Okabe ◽  
Kyoji Umemura ◽  
Seiji Yamada
Keyword(s):  
Query Expansion ◽  
User Feedback ◽  
Transductive Learning

The So-Called Prothetic i- and the sḏm-f Paradigms II: The “Nominal” sḏm-f and a Reappraisal

2020 ◽  
Vol 28 ◽  
pp. 259-267
Author(s):  
Sami Uljas
Keyword(s):  
Present Author ◽  
Textual Data ◽  
Key Issues ◽  
Syllabic Structure ◽  
The Creation ◽  
Partial Revision

This article discusses, first, the role of the i-prefix in the so-called “nominal” sḏm-f paradigm in earliest Old Egyptian textual data. It is argued that this represented a means of facilitating the creation of a distinctive syllabic structure with 2rad roots and of harmonising it with that of the 2red and 3inf classes. Second, the study contains a partial revision of some of the key issues treated in an earlier article by the present author on the role of the similarly written prefix in the subjunctive and “circumstantial” sḏm-f paradigms.

Corpus CesCa

2012 ◽  
Vol 17 (3) ◽  
pp. 428-441 ◽  
Author(s):  
Anna Llaurado ◽  
Maria Antònia Martí ◽  
Liliana Tolchinsky
Keyword(s):  
Written Language ◽  
Compulsory Schooling ◽  
Written Production ◽  
Semantic Fields ◽  
Textual Data

This paper outlines the compilation of a corpus of Catalan written production. The CesCa corpus presents a picture of the Catalan written language throughout compulsory schooling. It contains two kinds of data: Vocabularies of five semantic fields comprising 242,404 lexical forms and Textual data of four different discourse genres consisting of 207,028 tokens. Both vocabularies and the textual data have been morphologically analyzed and lemmatized. The corpus is freely available. This paper will outline the main features of the corpus and make some suggestions as to the uses to which the corpus can be put.

scholarly journals COVID-19 Pandemic Is About More than Health: A State of Governance Catastrophe in Bangladesh

2021 ◽  
pp. 097152312199334
Author(s):  
Khandakar Farid Uddin
Keyword(s):  
Data Analysis ◽  
Qualitative Methods ◽  
Economic Recovery ◽  
Journal Articles ◽  
Governance Model ◽  
Textual Data ◽  
Governance Strategies ◽  
Spread Of Infection

Governance can help minimise the effects of catastrophes. Countries had some time to prepare for the current coronavirus disease 2019 (COVID-19) pandemic, but some did not use it to improve their arrangements. This research investigates several countries’ governance strategies, develops a governance model and critically analyses Bangladesh’s failure as a case of governance catastrophe. This study applies qualitative methods of textual data analysis to explore data sourced from current newspapers, blogs, websites, journal articles and books to determine the most appropriate evidence and generate connections and interpretations. The COVID-19 pandemic has had devastating consequences for all countries; however, the different national responses have provided the opportunity to measure governments’ capability in addressing the crisis. Governments need to study the current COVID-19 response and enhance their governance capacities to minimise the spread of infection and to prepare for the challenge of socio-economic recovery.

scholarly journals Digitalization and reasoning over engineering textual data stored in spreadsheet tables

2020 ◽  
Vol 53 (3) ◽  
pp. 239-244
Author(s):  
Melinda Hodkiewicz ◽  
Johan W. Klüwer ◽  
Caitlin Woods ◽  
Thomas Smoker ◽  
Tim French
Keyword(s):  
Textual Data
Sign in / Sign up

Export Citation Format

Share Document