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.

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.

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.

Performance of Wireless Magnetometers for Stop Bar Detection at Signalized Intersections

2018 ◽  
Vol 2672 (45) ◽  
pp. 55-63 ◽  
Author(s):  
Jidong J. Yang ◽  
Bashan Zuo
Keyword(s):  
Environmental Conditions ◽  
Access Point ◽  
Signalized Intersections ◽  
Conditional Inference ◽  
Professional Judgment ◽  
Heavy Vehicles ◽  
Study Results ◽  
Adverse Weather ◽  
Reliable Detection ◽  
Conditional Inference Trees

Wireless magnetometers have been considered as a practical alternative to inductive loops and suitable for large intersections, where span wire is generally used for traffic signal support. In this paper, wireless magnetometers are evaluated for stop bar vehicle detection at signalized intersections. High-resolution detector data were collected in the field subjected to various weather and environmental conditions. Conditional inference trees were used to correlate detection errors with weather and environmental factors that potentially affect the performance of wireless magnetometers. The study results indicated that the wireless magnetometers are fairly robust to various environmental conditions, such as wind, lighting, and visibility. Frequent passing of heavy vehicles, common at large intersections, can cause communication interruption between in-pavement sensors and the access point. This likely increases false and stuck-on call errors, which could be aggravated by adverse weather (e.g., rain, fog, or snow). This communication interruption issue can be mitigated by proper installation of additional repeaters. Provided the interruption issues are site-specific, professional judgment and field test are required for proper system setup, which is critical to delivering accurate and reliable detection for the wireless magnetometer system.

scholarly journals Clustering of Authors’ Texts of English Fiction in the Vector Space of Semantic Fields

2014 ◽  
Vol 14 (3) ◽  
pp. 25-36
Author(s):  
Bohdan Pavlyshenko
Keyword(s):  
Cluster Analysis ◽  
Vector Space ◽  
Cluster Structure ◽  
Vector Space Model ◽  
Semantic Space ◽  
Field Vector ◽  
Semantic Field ◽  
Text Documents ◽  
Space Model ◽  
Semantic Fields

Abstract This paper describes the analysis of possible differentiation of the author’s idiolect in the space of semantic fields; it also analyzes the clustering of text documents in the vector space of semantic fields and in the semantic space with orthogonal basis. The analysis showed that using the vector space model on the basis of semantic fields is efficient in cluster analysis algorithms of author’s texts in English fiction. The study of the distribution of authors' texts in the cluster structure showed the presence of the areas of semantic space that represent the idiolects of individual authors. Such areas are described by the clusters where only one author dominates. The clusters, where the texts of several authors dominate, can be considered as areas of semantic similarity of author’s styles. SVD factorization of the semantic fields matrix makes it possible to reduce significantly the dimension of the semantic space in the cluster analysis of author’s texts. Using the clustering of the semantic field vector space can be efficient in a comparative analysis of author's styles and idiolects. The clusters of some authors' idiolects are semantically invariant and do not depend on any changes in the basis of the semantic space and clustering method.

The dative alternation in South Asian English(es)

2014 ◽  
Vol 35 (1) ◽  
pp. 7-31 ◽  
Author(s):  
Tobias Bernaisch ◽  
Stefan Th. Gries ◽  
Joybrato Mukherjee
Keyword(s):  
South Asian ◽  
Conditional Inference ◽  
The South ◽  
Double Object ◽  
Indian English ◽  
British English ◽  
Semantic Class ◽  
Dative Alternation ◽  
Syntactic Pattern ◽  
Conditional Inference Trees

The present paper focuses on the modelling of cross-varietal differences and similarities in South Asian English(es) and British English at the level of verb complementation. Specifically, we analyse the dative alternation with GIVE, i.e. the alternation between the double-object construction (John gave Mary a book) and the prepositional dative (John gave a book to Mary) as well as their passivised constructions with regard to the factors that potentially exert an influence on this alternation in seven varieties of English. The South Asian varieties under scrutiny are Bangladeshi English, Indian English, Maldivian English, Nepali English, Pakistani English and Sri Lankan English, while British English serves as the reference variety. The patterns of GIVE are annotated according to the following parameters including potential predictors of the dative alternation: syntactic pattern and semantic class of GIVE; syntactic complexity, animacy, discourse accessibility and pronominality of constituents (cf. Gries 2003b; Bresnan and Hay 2008). The choices of complementation patterns are then statistically modelled using conditional inference trees and a random-forest analysis. The results indicate that many of the predictors found to be relevant in British English are at play in the South Asian varieties, too. The syntactic pattern of GIVE is, in descending order, uniformly influenced by the predictors pronominality of recipient, length of recipient, semantic class of GIVE and length of patient. Interestingly, the predictor country is marginal in accounting for the dative alternation of GIVE across the varieties at hand. Based on this observation, we derive variety-independent protostructions, i.e. abstract combinations of (cross-varietally stable) features with high predictive power for a particular syntactic pattern, which we argue to be part of the lexicogrammatical “common core” (Quirk et al. 1985: 16) of English. The implications of the present paper are twofold. While the order of the predictors regarding their influence on the dative alternation is clearly compatible with earlier studies (cf. e.g. Green 1974; Ransom 1979; Hawkins 1994; Gries 2003b), the stability of the order across varieties of English calls for a) a more fine-grained gradation of linguistic forms and structures at the lexis-grammar interface as indicators of structural nativisation and b) a revision of earlier verb-complementational findings specific to individual or groups of varieties of South Asian English.

An ensemble feature method for food classification

2017 ◽  
Vol 26 (1) ◽  
pp. 13-39
Author(s):  
Niki Martinel ◽  
Christian Micheloni ◽  
Claudio Piciarelli
Keyword(s):  
Random Forest ◽  
State Of The Art ◽  
Texture Feature ◽  
Random Forest Classifier ◽  
Texture Feature Extraction ◽  
Feature Encoding ◽  
Food Recognition ◽  
Feature Based ◽  
Food Image ◽  
Comparative Results

In the last years, several works on automatic image-based food recognition have been proposed, often based on texture feature extraction and classification. However, there is still a lack of proper comparisons to evaluate which approaches are better suited for this specific task. In this work, we adopt a Random Forest classifier to measure the performances of different texture filter banks and feature encoding techniques on three different food image datasets. Comparative results are given to show the performance of each considered approach, as well as to compare the proposed Random Forest classifiers with other feature-based state-of-the-art solutions.

scholarly journals A Multivariate Study of T/V Forms in European Languages Based on a Parallel Corpus of Film Subtitles

2017 ◽  
Vol 15 (2) ◽  
pp. 153-172 ◽  
Author(s):  
Natalia Levshina
Keyword(s):  
Multivariate Analyses ◽  
The Other ◽  
Conditional Inference ◽  
Contextual Variables ◽  
Linguistic Differences ◽  
Parallel Corpus ◽  
Contextual Features ◽  
European Languages ◽  
Multivariate Study ◽  
Conditional Inference Trees

The present study investigates the cross-linguistic differences in the use of so-called T/V forms (e.g. French tu and vous, German du and Sie, Russian ty and vy) in ten European languages from different language families and genera. These constraints represent an elusive object of investigation because they depend on a large number of subtle contextual features and social distinctions, which should be cross-linguistically matched. Film subtitles in different languages offer a convenient solution because the situations of communication between film characters can serve as comparative concepts. I selected more than two hundred contexts that contain the pronouns you and yourself in the original English versions, which are then coded for fifteen contextual variables that describe the Speaker and the Hearer, their relationships and different situational properties. The creators of subtitles in the other languages have to choose between T and V when translating from English, where the T/V distinction is not expressed grammatically. On the basis of these situations translated in ten languages, I perform multivariate analyses using the method of conditional inference trees in order to identify the most relevant contextual variables that constrain the T/V variation in each language.

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