Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRegression Analysis of Lexical and Morpho-Syntactic Properties of Kiezdeutsch
تحليل الانحدار للعقارات المعجمية والمورفخنة من Kiezdeutsch
283
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
Kiezdeutsch هو مجموعة متنوعة من اللغة الألمانية التي يتحدثها المراهقون من الأحياء الحضرية متعددة الأعراق في محادثات عادية مع أقرانهم. في السنوات الأخيرة، زادت شعبية كيزدوتش بين الشباب، بشكل مستقل عن أصلها الاجتماعي والاقتصادي، وانتشر في وسائل التواصل الاجتماعي أيضا. في حين أن الدراسات السابقة تحققت على نطاق واسع في هذه المجموعة اللغوية من منظور لغوي ونوعي، فقد تم القيام بالكثير من وجهة نظر كمية. نقوم بإجراء أول تحليل مدفوع من البيانات على نطاق واسع من الخصائص المعجمية والمورفولوجية في Kiezdeutsch بالمقارنة مع الألمانية القياسية. على مستوى النتائج، نؤكد تنبؤات التحليلات النوعية السابقة ودمجها بمزيد من الملاحظات على ظواهر لغوية محددة مثل اللغات العامية وموقف المتكلم المرتبط بالنفس. على المستوى المنهجي، نقدم الانحدار اللوجستي كإطار لإجراء اختيار ميزة من أسفل إلى أسفل من أجل تحديد الاختلافات عبر أنواع اللغات.
Kiezdeutsch is a variety of German predominantly spoken by teenagers from multi-ethnic urban neighborhoods in casual conversations with their peers. In recent years, the popularity of Kiezdeutsch has increased among young people, independently of their socio-economic origin, and has spread in social media, too. While previous studies have extensively investigated this language variety from a linguistic and qualitative perspective, not much has been done from a quantitative point of view. We perform the first large-scale data-driven analysis of the lexical and morpho-syntactic properties of Kiezdeutsch in comparison with standard German. At the level of results, we confirm predictions of previous qualitative analyses and integrate them with further observations on specific linguistic phenomena such as slang and self-centered speaker attitude. At the methodological level, we provide logistic regression as a framework to perform bottom-up feature selection in order to quantify differences across language varieties.
References used
https://aclanthology.org/
rate research
Read More
The span-based model enjoys great popularity in recent works of sequence segmentation. However, each of these methods suffers from its own defects, such as invalid predictions. In this work, we introduce a unified span-based model, lexical unit analy
sis (LUA), that addresses all these matters. Segmenting a lexical unit sequence involves two steps. Firstly, we embed every span by using the representations from a pretraining language model. Secondly, we define a score for every segmentation candidate and apply dynamic programming (DP) to extract the candidate with the maximum score. We have conducted extensive experiments on 3 tasks, (e.g., syntactic chunking), across 7 datasets. LUA has established new state-of-the-art performances on 6 of them. We have achieved even better results through incorporating label correlations.
The real estate valuation is the process of estimating the real estate
price based on the real estate description and property features .
The aim of this research is to define the main variables and study
their effects on defining the price of the
residential real estates in
Damascus, hence to build a mathematical model based on Multiple
Regression Analysis method for the valuation of these Real Estates
Data on 130 residential real estates in Damascus were collected
and analyzed using the SPSS package .
The Analyses have revealed that it is possible to rely on the
Multiple Regression Analyses model to estimate the price of the
real estate but only after defining and testing its shortcomings and
transforming the model into Multiple – Non linear Regression
model .
This research concluded that the relationship between the price of
the real estate and the studied variables is nonlinear .
Linear regression methods impose strong constraints on regression models, especially on
the error terms where it assumes that it is independent and follows normal distribution, and
this may not be satisfied in many studies, leading to bias that can
not be ignored from the
actual model, which affects the credibility of the study.
We present in this paper the problem of estimating the regression function using the
Nadarya Watson kernel and k- nearest neighbor estimators as alternatives to the parametric
linear regression estimators through a simulation study on an imposed model, where we
conducted a comparative study between these methods using the statistical programming
language R in order to know the best of these estimations. Where the mean squares errors
(MSE) was used to determine the best estimate.
The results of the simulation study also indicate the effectiveness and efficiency of the
nonparametric in the representation of the regression function as compared to linear
regression estimators, and indicate the convergence of the performance of these two
estimates.
The modern era is witnessing a tangible development in all fields of science . As a result of this development , there is a growing need for statistical methods to solve the problems facing workers in these fields.
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
