Development of adjective frequencies across semantic classes

2014 ◽  
Vol 5 (2) ◽  
pp. 185-226 ◽  
Author(s):  
Elena Tribushinina ◽  
Huub van den Bergh ◽  
Dorit Ravid ◽  
Ayhan Aksu-Koç ◽  
Marianne Kilani-Schoch ◽  
...  
Keyword(s):  
Logistic Regression ◽  
First Year ◽  
Multilevel Logistic Regression ◽  
Semantic Class ◽  
Semantic Classes ◽  
Longitudinal Investigation ◽  
Children And Parents ◽  
Child Speech ◽  
Concrete Concepts ◽  
Adjective Acquisition

This paper is a longitudinal investigation of adjective use by children aged 1;8−2;8, speaking Dutch, German, French, Hebrew, and Turkish, and by their caregivers. Each adjective token in transcripts of spontaneous speech was coded for semantic class. The development of adjective use in each semantic class was analysed by means of a multilevel logistic regression. The results show that toddlers and their parents use adjectives more often as the child grows older. However, this holds only for semantic classes denoting concrete concepts, such as physical properties, colour, and size. Adjectives denoting more abstract properties are barely used by children and parents throughout the first year of adjective acquisition. The correlations between adjective frequencies in child speech and child-directed speech are very strong at the beginning, but decrease with time as the child develops independent adjective use. The composition of early adjective lexicons is very similar in the five languages under study.

A Longitudinal Investigation of First-Generation College Students’ Mentoring Relationships During Their Transition to Higher Education

2021 ◽  
pp. 152102512110227
Author(s):  
Matthew A. Hagler ◽  
Kirsten M. Christensen ◽  
Jean E. Rhodes
Keyword(s):  
College Students ◽  
Self Efficacy ◽  
Help Seeking ◽  
First Generation ◽  
First Generation College Students ◽  
Mentoring Relationships ◽  
First Year ◽  
School Membership ◽  
Longitudinal Investigation ◽  
First Generation College

Non-parent mentoring relationships are important protective factors for first-generation college students. Previous research has focused on singular mentoring relationships measured at one time point, failing to capture the breadth and dynamic nature of social networks. The current study is a longitudinal investigation of first-generation students’ mentoring networks during their transition to college at a four-year, predominantly minority-serving commuter university. At the beginning and end of their first year, students (N = 176) responded to online surveys on their mentoring relationship(s), attitudes toward help-seeking, and college experiences. Cumulative support from pre-college mentoring relationships retained across the first year was positively associated with self-efficacy. Support from newly acquired mentoring relationships was positively associated with psychological sense of school membership. Network orientation was positively associated with self-efficacy and sense of school membership. These findings highlight the importance of diverse mentoring networks and demonstrate the utility of collecting longitudinal data on multiple mentoring relationships.

scholarly journals Factors associated with depressive episode among first year University students in Serbia

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S Čanković ◽  
D Čanković ◽  
I Radić ◽  
D Rakić ◽  
S Ukropina ◽  
...  
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Depressive Symptoms ◽  
University Students ◽  
Depressive Episode ◽  
Social Health ◽  
Support Network ◽  
First Year ◽  
Behavioral Factors ◽  
First Year University

Abstract Background Some findings indicate that university students are at higher risk of depression despite being a socially advantaged population, showing that 15 to 25% of them develop some type of mental disorder during graduation, and depression is one of the most prevalent. The aim of this study was to investigate the prevalence of depressive symptoms and its association with gender, material status, social health, and behavioral factors among university students. Methods The research represents a cross-sectional study conducted at the University of Novi Sad, Serbia. The research involved 455 first-year students. The average age of participants was 19.3 years (72.1% girls and 27.9% boys). Patient Health Questionnaire (PHQ-9) was used to measure depressive symptoms (cut point for depressive episode ≥10). Additional questions were focused on self-assessed material status, social health, and behavioral factors (smoking and alcohol use). The binary logistic regression model was used to estimate the association between depression and observed independent variables. Results The prevalence of moderate to severe depressive episode among students was 18.0%, with no gender differences. Out of the total, 11.0% of students had suicidal thoughts or thoughts of hurting themselves. Multivariable logistic regression showed that self-assessed poor material status (OR = 2.04; 95%CI: 1.06-3.89), self-assessed poor social health (OR = 8.9; 95%CI:4.87-16.42) and smoking (OR = 2.18; 95%CI: 1.09-4.39) were significant predictors of depression among first-year university students. Conclusions High prevalence of depressive episode among university students call for the action for the promotion of mental health, particularly among those of lower socioeconomic status, and points out the importance of social health and social network in the prevention of mental disorders. Key messages Students on the first year of University should be screened for depressive symptoms. It is important to educate students on how to create a strong support network and to implement intervention programs for improving mental health through fostering healthy lifestyles change.

scholarly journals Schaalvergroting in het syntactische alternantieonderzoek : Een nieuwe analyse van het presentatieve er met automatisch gegenereerde predictoren

2020 ◽  
Vol 25 (1) ◽  
pp. 101-123
Author(s):  
Dirk Speelman ◽  
Stefan Grondelaers ◽  
Benedikt Szmrecsanyi ◽  
Kris Heylen
Keyword(s):  
The Other ◽  
Manual Annotation ◽  
Distributional Analysis ◽  
Syntactic Variation ◽  
Semantic Class ◽  
Semantic Classes ◽  
Reference Corpus ◽  
Lexical Collocations ◽  
The Subject ◽  
Pragmatic Factor

Abstract In this paper, we revisit earlier analyses of the distribution of er ‘there’ in adjunct-initial sentences to demonstrate the merits of computational upscaling in syntactic variation research. Contrary to previous studies, in which major semantic and pragmatic predictors (viz. adjunct type, adjunct concreteness, and verb specificity) had to be coded manually, the present study operationalizes these predictors on the basis of distributional analysis: instead of hand-coding for specific semantic classes, we determine the semantic class of the adjunct, verb, and subject automatically by clustering the lexemes in those slots on the basis of their ‘semantic passport’ (as established on the basis of their distributional behaviour in a reference corpus). These clusters are subsequently interpreted as proxies for semantic classes. In addition, the pragmatic factor ‘subject predictability’ is operationalized automatically on the basis of collocational attraction measures, as well as distributional similarity between the other slots and the subject. We demonstrate that the distribution of er can be modelled equally successfully with the automated approach as in manual annotation-based studies. Crucially, the new method replicates our earlier findings that the Netherlandic data are easier to model than the Belgian data, and that lexical collocations play a bigger role in the Netherlandic than in the Belgian data. On a methodological level, the proposed automatization opens up a window of opportunities. Most important is its scalability: it allows for a larger gamut of alternations that can be investigated in one study, and for much larger datasets to represent each alternation.

scholarly journals Predictive Analysis of Higher Education Graduation and Retention in Saudi Arabia u sing Multinomial Logistic Regression

2021 ◽  
Vol 3 (6) ◽  
pp. 1-8
Author(s):  
Ahmed Bagabir ◽  
◽  
Mohammad Zaino ◽  
Ahmed Abutaleb ◽  
Ahmed Fagehi ◽  
...  
Keyword(s):  
Higher Education ◽  
Logistic Regression ◽  
Undergraduate Students ◽  
Degree Completion ◽  
Multinomial Logistic Regression ◽  
At Risk Students ◽  
Retention Rates ◽  
First Year ◽  
Important Indicator ◽  
Admissions Policy

It is suggested that this study contributes by establishing a robust methodology for analyzing the longitudinal outcomes of higher education. The current research uses multinomial logistic regression. To the knowledge of the authors, this is the first logistic regression analysis performed at Saudi higher education institutions. The study can help decision-makers take action to improve the academic performance of at-risk students. The analyses are based on enrollment and completion data of 5,203 undergraduate students in the colleges of engineering and medicine. The observation period was extended for ten academic years from 2010 to 2020. Four outcomes were identified for students: (i) degree completion on time, (ii) degree completion with delay, (iii) dropout, and (iv) still enrolled in programs. The objectives are twofold: (i) to study the present situation by measuring graduation and retention rates with benchmarking, and (ii) to determine the effect of twelve continuous and dummy predictors (covariates) on outcomes. The present results show that the pre-admission covariates slightly affect performance in higher education programs. The results indicate that the most important indicator of graduation is the student's achievement in the first year of the program. Finally, it is highly suggested that initiatives be taken to increase graduation and retention rates and to review the admissions policy currently in place.

scholarly journals Word vs. Class-Based Word Sense Disambiguation

2015 ◽  
Vol 54 ◽  
pp. 83-122 ◽  
Author(s):  
Ruben Izquierdo ◽  
Armando Suarez ◽  
German Rigau
Keyword(s):  
Word Sense Disambiguation ◽  
Coarse Grained ◽  
Semantic Features ◽  
Word Sense ◽  
Simple Method ◽  
Word Meanings ◽  
Semantic Class ◽  
Semantic Classes ◽  
Sense Disambiguation ◽  
Word Senses

As empirically demonstrated by the Word Sense Disambiguation (WSD) tasks of the last SensEval/SemEval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. Many authors argue that one possible reason could be the use of inappropriate sets of word meanings. In particular, WordNet has been used as a de-facto standard repository of word meanings in most of these tasks. Thus, instead of using the word senses defined in WordNet, some approaches have derived semantic classes representing groups of word senses. However, the meanings represented by WordNet have been only used for WSD at a very fine-grained sense level or at a very coarse-grained semantic class level (also called SuperSenses). We suspect that an appropriate level of abstraction could be on between both levels. The contributions of this paper are manifold. First, we propose a simple method to automatically derive semantic classes at intermediate levels of abstraction covering all nominal and verbal WordNet meanings. Second, we empirically demonstrate that our automatically derived semantic classes outperform classical approaches based on word senses and more coarse-grained sense groupings. Third, we also demonstrate that our supervised WSD system benefits from using these new semantic classes as additional semantic features while reducing the amount of training examples. Finally, we also demonstrate the robustness of our supervised semantic class-based WSD system when tested on out of domain corpus.

A Longitudinal Investigation of Dropout from College in the United States

2002 ◽  
Vol 4 (2) ◽  
pp. 173-201 ◽  
Author(s):  
Terry T. Ishitani ◽  
Stephen L. DesJardins
Keyword(s):  
United States ◽  
College Students ◽  
Financial Aid ◽  
The United States ◽  
Dropout Rates ◽  
First Year ◽  
Student Financial Aid ◽  
Student Dropout ◽  
Longitudinal Investigation ◽  
Over Time

This study investigates the dropout behavior of college students in the United States. Previous attrition studies have typically focused on dropout at specific points in time, such as the first year of enrollment. In this study we examine the timing of dropout over a five-year period and find that factors that affect student dropout often have effects that change over time. For instance, the results demonstrate that students who receive financial aid generally have lower dropout rates than non-aided students. But of special interest is our findings that dropout rates vary depending on the amount and timing of student financial aid.

Household means of transportation and place of child delivery in Nigeria

2020 ◽  
Author(s):  
Lukman Bola Solanke ◽  
Omolayo Bukola Oluwatope ◽  
Yinusa Rasheed Adebayo ◽  
Olaoye James Oyeleye ◽  
Benjamin Bukky Ilesanmi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Pregnant Women ◽  
Logistic Regression Model ◽  
Institutional Delivery ◽  
Multilevel Logistic Regression ◽  
Place Of Delivery ◽  
Pooled Data ◽  
Support Programme ◽  
Mode Of Transport ◽  
Multilevel Logistic Regression Model

Abstract Background The means of transportation available to pregnant women in households may serve either as a driver or deterrent of institutional delivery. However, how household means of transportation associates with place of delivery has been less explored in Nigeria. Methods This study was based on pooled data of 2008-2013 Nigeria Demographic and Health Survey. The study analysed a weighted sample size of 6,540 women. The multilevel logistic regression model was applied using STATA 14. Results The study revealed 37% institutional delivery among women in Nigeria. Women whose household mode of transport were cars were twice more likely to have institutional delivery compared to women who had no viable household means of transportation (AOR=2.044, p<0.01; CI=1.781-2.345). Women who live in communities with high proportions of households with no means of transportation were 12.8% less likely to have institutional delivery (AOR=0.872, p=0.01; CI: 0.788-0.967). Women who live in communities with high proportions of household who owned motorcycle compared to those in communities with low proportion were 31.9% more likely to have institutional delivery (AOR=1.319, p<0.05; CI: 1.071-1.625). Women who live in communities with high proportions of households who owned cars compared to those in communities with low proportion were more than three times more likely to have institutional delivery (AOR=3.146, p<0.01; CI: 2.621-3.777).Conclusion Means of transportation significantly explains choice of place of child delivery in urban Nigeria. A public-private transport support programme to reduce transportation burden among pregnant women is imperative in the country.

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