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Exploring individual variation in Turkish heritage speakers’ complex linguistic productions: Evidence from discourse markers ...
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LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining
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In: Bioengineering; Volume 9; Issue 3; Pages: 124 (2022)
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Linguistic Mathematical Relationships Saved or Lost in Translating Texts: Extension of the Statistical Theory of Translation and Its Application to the New Testament
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In: Information; Volume 13; Issue 1; Pages: 20 (2022)
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Predicting Academic Performance: Analysis of Students’ Mental Health Condition from Social Media Interactions
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In: Behavioral Sciences; Volume 12; Issue 4; Pages: 87 (2022)
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Using Geolocated Text to Quantify Location in Real Estate Appraisal
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Cross-Sectional Analysis of Methods of Computing Partial Correlation Coefficients: A Self-Explained Note With R Syntax
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In: Cross-Cultural Communication; Vol 18, No 1 (2022): Cross-Cultural Communication; 18-31 ; 1923-6700 ; 1712-8358 (2022)
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THE EFFECTS OF L2 PRAGMATIC AUTONOMOUS AND CONTROLLED MOTIVATIONS ON ENGAGEMENT WITH PRAGMATIC ASPECT
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In: TEFLIN Journal, Vol 33, Iss 1, Pp 148-172 (2022) (2022)
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Tackling Morphological Analogies Using Deep Learning -- Extended Version
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In: https://hal.inria.fr/hal-03425776 ; 2021 (2021)
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A dimension reduction technique applied to regression on high dimension, low sample size neurophysiological data sets
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In: ISSN: 1471-2202 ; EISSN: 1471-2202 ; BMC Neuroscience ; https://hal.univ-grenoble-alpes.fr/hal-03374818 ; BMC Neuroscience, BioMed Central, 2021, 22 (1), ⟨10.1186/s12868-020-00605-0⟩ (2021)
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| 11 |
Pragmatic Influences on Argument Word Order in Karuk Narrative Texts
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In: Berkeley Papers in Formal Linguistics, vol 3, iss 1 (2021)
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A logistic regression model for predicting child language performance ; Un modèle de régression logistique pour la prédiction du développement langagier chez l'enfant
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In: SIS 2021, 50th Annuale Conference of the Italian Statistical Society" ; https://hal.archives-ouvertes.fr/hal-03318721 ; SIS 2021, 50th Annuale Conference of the Italian Statistical Society", Jun 2021, Pise, Italy (2021)
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| 13 |
Native word order processing is not uniform: An ERP-study of verb-second word order ...
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| 14 |
A multi-method approach to correlate identification in acoustic data: The case of Media Lengua
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In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 12, No 1 (2021); 13 ; 1868-6354 (2021)
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The Orange workflow for observing collocation trends ColTrend 1.0
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Kosem, Iztok; Krek, Simon; Čibej, Jaka; Gantar, Polona; Arhar Holdt, Špela; Logar, Nataša; Laskowski, Cyprian; Klemenc, Bojan; Ljubešić, Nikola; Dobrovoljc, Kaja; Gorjanc, Vojko; Pori, Eva. - : Centre for Language Resources and Technologies, University of Ljubljana, 2021
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Abstract:
The Orange workflow for observing collocation trends ColTrend 1.0 ColTrend is a workflow (.OWS file) for Orange Data Mining (an open-source machine learning and data visualization software: https://orangedatamining.com/) that allows the user to observe temporal collocation trends in corpora. The workflow consists of a series of Python scripts, data filters, and visualizers. As input, the workflow takes a .CSV file with data on collocations and their relative frequencies by year of publication extracted from a corpus. As output, it provides a .TSV file containing the same data (or a filtered selection thereof) enriched with four measures that indicate the collocation’s temporal trend in the corpus: (1) the slope (k) of a linear regression model fitted to the frequency data, which indicates whether the frequency of use of the collocation is increasing or declining; (2) the coefficient of determination (R2) of the linear regression model, indicating how linear the change in the collocation’s use is; (3) the ratio (m) of maximum relative frequency and average relative frequency, which indicates peaks in collocation usage; and (4) the coefficient of recent growth (t), which indicates an increased usage of the collocation in the last three years of the observed corpus data. The entry also contains three .CSV files that can be used to test the workflow. The files contain collocation candidates (along with their relative frequencies per year of publication) extracted from the Gigafida 2.0 Corpus of Written Slovene (https://viri.cjvt.si/gigafida/) with three different syntactic structures (as defined in http://hdl.handle.net/11356/1415): 1) p0-s0 (adjective + noun, e.g. rezervni sklad), 2) s0-s2 (noun + noun in the genitive case, e.g. ukinitev lastnine), and 3) gg-s4 (verb + noun in the accusative case, e.g. pripraviti besedilo). It should be noted that only collocation candidates with absolute frequency of 15 and above were extracted. Please note that the ColTrend workflow requires the installation of the Text Mining add-on for Orange. For installation instructions as well as a more detailed description of the different phases of the workflow and the measures used to observe the collocation trends, please consult the README file.
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Keyword:
collocations; linear regression; relative frequency; temporal trends
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URL: http://hdl.handle.net/11356/1424
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Which data do elementary school teachers use to determine reading difficulties in their students?
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In: Journal of learning disabilities 54 (2021) 5, S. 349-364 (2021)
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STUDENT ACHIEVEMENT INDICATORS AT DEFENSE LANGUAGE INSTITUTE FOREIGN LANGUAGE CENTER
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Which data do elementary school teachers use to determine reading difficulties in their students? ...
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