Vowel duration in English adjectives in attributive and predicative constructions

2019 ◽  
Vol 11 (4) ◽  
pp. 555-581
Author(s):  
JOAN BYBEE ◽  
RICARDO NAPOLEÃO DE SOUZA
Keyword(s):  
Random Forest ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Conditional Inference ◽  
Statistical Techniques ◽  
Vowel Duration ◽  
Number Of Factors ◽  
Conditional Inference Trees ◽  
Construction Type ◽  
As Effects

abstractUsing ten English adjectives, this study tests the hypothesis that the vowels in adjectives in predicative constructions are longer than those in attributive constructions in spoken conversation. The analyses considered a number of factors: occurrence before a pause, lexical adjective, vowel identity, probability given surrounding words, and others. Two sets of statistical techniques were used: a Mixed-effects model and the Random Forest Analysis based on Conditional Inference Trees (CIT). Both analyses showed strong effects of predicative vs. attributive constructions and individual lexical adjectives on vowel duration in the predicted direction, as well as effects of many of the phonological variables tested. The results showed that the longer duration in the predicative construction is not due to lengthening before a pause, though it is related to whether the adjective is internal or final in the predicative construction. Nor is the effect attributable solely to the probability of the occurrence of the adjective; rather construction type has to be taken into account. The two statistical techniques complement each other, with the Mixed-effects model showing very general trends over all the data, and the Random Forest / CIT analysis showing factors that affect only subsets of the data.

Zur Entwicklung der modalenverstehen-Konstruktion: Ein Konservierungseffekt im Zuge der Auxiliarisierung

2020 ◽  
Vol 48 (1) ◽  
pp. 101-135
Author(s):  
Volodymyr Dekalo
Keyword(s):  
Random Forest ◽  
20Th Century ◽  
Semantic Space ◽  
Time Span ◽  
Conditional Inference ◽  
Fourth Period ◽  
Space Model ◽  
Previous Time ◽  
Conditional Inference Trees ◽  
Feature Based

AbstractThe present paper deals with item- and feature-based changes of the modal semi-schematic construction with verstehen in written German during the 20th century. To understand this development, the century is divided into four equal periods. Applying a simple collexeme analysis for each time span, the study ascertains which lexical verbs appear as typical items in a schematic slot constituting its collostructional profile. Comparing the distributional behavior manually in a pairwise fashion, the analysis reveals that solely three verbs, namely machen, umgehen and meistern, stay constantly highly attracted within the top collexemes of the verstehen-construction during the 20th century. Using a dependency-based semantic space model, the study demonstrates that the collostructional profile of the fourth period differs considerably from the previous time span. Utilizing random forest of conditional inference trees, changes in terms of usage features of the modal construction are pinpointed. As a result, its grammaticality degree has not increased demonstrating solely minor changes in temporal functionality as well as in realization of subject forms.

scholarly journals Discourse-Pragmatic and Processing-Related Motivators of the ordering of Reason Clauses in an Academic Corpus

2018 ◽  
Vol 16 (3) ◽  
pp. 325-339
Author(s):  
Abbas A. Rezaee ◽  
Majid Nemati ◽  
Seyyed Ehsan Golparvar
Keyword(s):  
Random Forest ◽  
Applied Linguistics ◽  
Statistical Modelling ◽  
Conditional Inference ◽  
Research Articles ◽  
Relative Importance ◽  
Conditional Inference Trees

The present research is aimed at examining the relative importance of the competing motivators of the sequencing of reason clauses in a corpus of research articles of applied linguistics. All the finite reason clauses accompanied by their main clauses in this corpus were collected. Random forest of conditional inference trees is the statistical modelling in this study. The findings showed that sentence-final reason clauses outnumber sentenceinitial ones. Moreover, subordinator choice and bridging, which are discourse-pragmatic constraints on clause positioning, emerged as the two more powerful predictors of the ordering of reason clauses in this corpus. Furthermore, the complexity of the clause turned out to be a stronger processing related predictor than the length of the clause.

scholarly journals Predicting poverty. Data mining approaches to the health and demographic surveillance system in Cuatro Santos, Nicaragua

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
C. Källestål ◽  
E. Blandón Zelaya ◽  
R. Peña ◽  
W. Peréz ◽  
M. Contreras ◽  
...  
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Poverty Reduction ◽  
Lessons Learned ◽  
Conditional Inference ◽  
Household Level ◽  
Piped Water ◽  
Rural Settings ◽  
Conditional Inference Trees ◽  
Basic Need

Abstract Background In order to further identify the needed interventions for continued poverty reduction in our study area Cuatro Santos, northern Nicaragua, we aimed to elucidate what predicts poverty, measured by the Unsatisfied Basic Need index. This analysis was done by using decision tree methodology applied to the Cuatro Santos health and demographic surveillance databases. Methods Using variables derived from the health and demographic surveillance update 2014, transferring individual data to the household level we used the decision tree framework Conditional Inference trees to predict the outcome “poverty” defined as two to four unsatisfied basic needs using the Unsatisfied Basic Need Index. We further validated the trees by applying Conditional random forest analyses in order to assess and rank the importance of predictors about their ability to explain the variation of the outcome “poverty.” The majority of the Cuatro Santos households provided information and the included variables measured housing conditions, assets, and demographic experiences since the last update (5 yrs), earlier participation in interventions and food security during the last 4 weeks. Results Poverty was rare in households that have some assets and someone in the household that has a higher education than primary school. For these households participating in the intervention that installed piped water with water meter was most important, but also when excluding this variable, the resulting tree showed the same results. When assets were not taken into consideration, the importance of education was pronounced as a predictor for welfare. The results were further strengthened by the validation using Conditional random forest modeling showing the same variables being important as predicting the outcome in the CI tree analysis. As assets can be a result, rather than a predictor of more affluence our results in summary point specifically to the importance of education and participation in the water installation intervention as predictors for more affluence. Conclusion Predictors of poverty are useful for directing interventions and in the Cuatro Santos area education seems most important to prioritize. Hopefully, the lessons learned can continue to develop the Cuatro Santos communities as well as development in similar poor rural settings around the world.

Models, forests, and trees of York English: Was/were variation as a case study for statistical practice

2012 ◽  
Vol 24 (2) ◽  
pp. 135-178 ◽  
Author(s):  
Sali A. Tagliamonte ◽  
R. Harald Baayen
Keyword(s):  
Random Forests ◽  
Linear Models ◽  
Random Effect ◽  
Mixed Effects ◽  
Conditional Inference ◽  
Mixed Effects Models ◽  
Future Research ◽  
Recent Developments ◽  
Conditional Inference Trees ◽  
Complementary Techniques

AbstractWhat is the explanation for vigorous variation between was and were in plural existential constructions, and what is the optimal tool for analyzing it? Previous studies of this phenomenon have used the variable rule program, a generalized linear model; however, recent developments in statistics have introduced new tools, including mixed-effects models, random forests, and conditional inference trees that may open additional possibilities for data exploration, analysis, and interpretation. In a step-by-step demonstration, we show how this well-known variable benefits from these complementary techniques. Mixed-effects models provide a principled way of assessing the importance of random-effect factors such as the individuals in the sample. Random forests provide information about the importance of predictors, whether factorial or continuous, and do so also for unbalanced designs with high multicollinearity, cases for which the family of linear models is less appropriate. Conditional inference trees straightforwardly visualize how multiple predictors operate in tandem. Taken together, the results confirm that polarity, distance from verb to plural element, and the nature of the DP are significant predictors. Ongoing linguistic change and social reallocation via morphologization are operational. Furthermore, the results make predictions that can be tested in future research. We conclude that variationist research can be substantially enriched by an expanded tool kit.

scholarly journals A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption

2020 ◽  
Vol 39 (15) ◽  
pp. 2051-2066 ◽  
Author(s):  
Rui Wang ◽  
Ante Bing ◽  
Cathy Wang ◽  
Yuchen Hu ◽  
Ronald J. Bosch ◽  
...  
Keyword(s):  
Viral Load ◽  
Treatment Interruption ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Nonlinear Mixed Effects ◽  
Hiv Viral Load ◽  
Nonlinear Mixed Effects Model ◽  
Viral Load Rebound

scholarly journals A Random Forest Model for the Prediction of FOG Content in Inlet Wastewater from Urban WWTPs

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1237
Author(s):  
Vanesa Mateo Pérez ◽  
José Manuel Mesa Fernández ◽  
Joaquín Villanueva Balsera ◽  
Cristina Alonso Álvarez
Keyword(s):  
Random Forest ◽  
Wastewater Treatment Plants ◽  
Learning Algorithm ◽  
Global Improvement ◽  
Treatment Processes ◽  
Early Adoption ◽  
Novel Approach ◽  
Number Of Factors ◽  
Initial Stage ◽  
Correct Design

The content of fats, oils, and greases (FOG) in wastewater, as a result of food preparation, both in homes and in different commercial and industrial activities, is a growing problem. In addition to the blockages generated in the sanitary networks, it also represents a difficulty for the performance of wastewater treatment plants (WWTP), increasing energy and maintenance costs and worsening the performance of downstream treatment processes. The pretreatment stage of these facilities is responsible for removing most of the FOG to avoid these problems. However, so far, optimization has been limited to the correct design and initial installation dimensioning. Proper management of this initial stage is left to the experience of the operators to adjust the process when changes occur in the characteristics of the wastewater inlet. The main difficulty is the large number of factors influencing these changes. In this work, a prediction model of the FOG content in the inlet water is presented. The model is capable of correctly predicting 98.45% of the cases in training and 72.73% in testing, with a relative error of 10%. It was developed using random forest (RF) and the good results obtained (R2 = 0.9348 and RMSE = 0.089 in test) will make it possible to improve operations in this initial stage. The good features of this machine learning algorithm had not been used, so far, in the modeling of pretreatment parameters. This novel approach will result in a global improvement in the performance of this type of facility allowing early adoption of adjustments to the pretreatment process to remove the maximum amount of FOG.

scholarly journals The state of Spanish /s/ variation in Concepción, Chile: Linguistic and social trends

2020 ◽  
Vol 6 (1) ◽  
pp. 132-153
Author(s):  
Brandon M. A. Rogers
Keyword(s):  
Social Factors ◽  
Central City ◽  
Mixed Effects ◽  
The State ◽  
Mixed Effects Model ◽  
Proportional Odds ◽  
Random Factor ◽  
Social Trends ◽  
Southern Central ◽  
Social Demographics

AbstractThe current study examines /s/ variation in the southern-central city of Concepción, Chile and its relation to a variety of linguistic and social factors. A proportional-odds mixed effects model, with the random factor of “speaker”, was used to treat the categorically coded data on a continuum of acoustical variation ([s] > [h] > ∅). The results presented show that contrary to the previous assertions, heavy sibilant reduction, especially elision, in Concepción, Chile is the rule, rather than the exception, to the extent that it is no longer a marker of certain social demographics as has been reported previously. Furthermore, based on the trends reported, it is likely that this has been the case for several decades. Finally, the overall observed trends are indicative that the rates of /s/ elision will continue to increase across social demographics and different phonetic and phonological contexts in Concepción, Chile.

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