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The use of the selected lexical and function words to, there, before by L2 Polish users at different proficiency levels

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Świątek A.
Lingwistyka Stosowana / Applied Linguistics / Angewandte Linguistik
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2017
|
issue 21
167-173
EN
The objective of this article will be both the theoretical and the practical analysis of the use of the selected English lexical and function words (to, there, before) by Polish users of English representing different proficiency levels. The practical part, in particular, will concern the use of the afore-mentioned, selected words comprising both categories based on the available corpus of L2 users (PELCRA – PLEC). The first acronym refers to Polish and English Language and Corpora for Research and Applications (PELCRA), which is the result of scientific work of the scholars from the University of Łódź. The other one (PLEC), stands for PELCRA English Learner Corpus (see the Bibliography section below). It is commonly known that learners/ students may demonstrate their awareness and professional knowledge with reference to the basic use and comprehension of words in their primary meaning, namely lexical words. The problems arise when they need to confront their current knowledge and awareness while discovering that some words are ambiguous or polysemous. Hence the idea of such research, the intention of which is to validate the students’ performance when faced with words, which are only seemingly simple and predictable.
2

Principal component analysis for authorship attribution

72%
Jamak A., Savatić A., Can M.
Business Systems Research Journal
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2012
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vol. 3
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issue 2
49-56
EN
Background: To recognize the authors of the texts by the use of statistical tools, one first needs to decide about the features to be used as author characteristics, and then extract these features from texts. The features extracted from texts are mostly the counts of so called function words. Objectives: The data extracted are processed further to compress as a data with less number of features, such a way that the compressed data still has the power of effective discriminators. In this case feature space has less dimensionality then the text itself. Methods/Approach: In this paper, the data collected by counting words and characters in around a thousand paragraphs of each sample book, underwent a principal component analysis performed using neural networks. Once the analysis was complete, the first of the principal components is used to distinguish the books authored by a certain author. Results: The achieved results show that every author leaves a unique signature in written text that can be discovered by analyzing counts of short words per paragraph. Conclusions: In this article we have demonstrated that based on analyzing counts of short words per paragraph authorship could be traced using principal component analysis. Methodology could be used for other purposes, like fraud detection in auditing.
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Перевод как способ толкования значений служебного слова

72%
Цой А.
Acta Universitatis Lodziensis. Folia Linguistica Rossica
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2018
|
issue 15
137-144
EN
It is necessary to divide the lemma in the academic explanatory dictionary into two parts in order to improve its quality. The Russian function word (source language) is introduced in the left with all lexicographic items. The right side (target language) of the dictionary entry contains necessary interpretation, lit­ters, examples in the fixed accordance with the source language entry At the same time, the source Russian version will always be fixed, and the target language input can be filled in English, German, Spanish, French, Turkish or Polish translations respectively.
RU
Для улучшения качественных характеристик словарной статьи учебного толкового словаря представляется целесообразным разделить ее на две части. В левой стороне (входной язык) помещается русское служебное слово со всем набором лексикографического инвентаря. Правая сторона (выходной язык) словарной статьи, в строгом соответствии с левой, дает необходимое толкование, пометы, примеры и прочее на иностранном языке. При этом «русская» левая часть всегда будет стандартной, а «инонациональная» часть может наполняться, соответственно, английским, немецким, испанским, французским, турецким или польским переводами.
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