title: Statistical analysis of near-synonymous words list and catalog in R
metadata.name: dc.contributor.author
metadata.value: Nosov, Andrei V.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2018-11-07T15:47:20Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2018-11-07T15:47:20Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2018-09
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Nosov A. V. Statistical analysis of near-synonymous words list and catalog in R. Vestnik of Saint Petersburg University. Language and Literature, 2018, vol. 15, issue 3, pp. 453–464.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/spbu09.2018.310
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/15045
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: In this article, we present the results of the regression analysis of near-synonymous words
  list and catalog. The purpose of the case study is allocation of the most objective variant by
  modeling the grammatical interactions that make impact on updating of the considered words.
  Determination of list and catalog as objective and independent lexical units is performed
  within the system of distinctions and oppositions. By the probabilistic distribution, we allocate
  two most frequent interactions. The comparison of average values does not reveal regularly all
  aspects of the studied phenomenon (i.e. average values of models can be statistically identical).
  Therefore, we compare the models with predictors PRE.MOD and GENITIVE MEAN with
  the model without interactions to show distinction between them at the level of dispersion.
  Hence, three statistical hypotheses are compared in pairs. The main says that dispersions
  of three considered models are statistically equal and the alternative affirms that they are
  different. Model assessment without interactions is a predictive logit of list. Coefficients
  of logistic regression reflect the probability of changes within interactions. At the stage of
  normalization, we apply the model of the binary choice Hosmer—Lemeshow. Based on the
  obtained results we decide whether it is necessary further normalization or not. We define
  also the presence/absence of correlated samples among the considered predictors by lrm
  function, which determines reliability of the model and allows receiving confidential intervals
  of coefficients. This approach reflects novelty of work and allows revealing the factors defining
  the choice of one or another concept proceeding from objective semantic criteria. Interactions
  are considered at four levels: academic, spoken, fiction and news. Results of research allow to
  complete the content of the words list and catalog and to present their dynamics.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Language and Literature;Volume 15; Issue 3
metadata.lang: -
metadata.name: dc.subject
metadata.value: computational linguistic
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: logistic regression
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: comparative analysis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: semantics
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: synonym
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: list
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: catalog
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Statistical analysis of near-synonymous words list and catalog in R
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 10-Nosov.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/15045/1/10-Nosov.pdf
resources.format: Adobe PDF
resources.size: 885,25 kB