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Linguistic Data: �Quantitative Analysis and Visualisation

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Lecture 1. Introduction

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Olga Lyashevskaya

olesar@yandex.ru

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HSE University, Moscow, MA program in Computational Linguistics

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Objectives

Examining linguistic data

• Descriptive stats
• Data visualization
• Hypothesis testing
• Data modeling

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Doing linguistic research

• Data collection
• Research design
• Data interpretation

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Practice in

Focus on

• Real linguistic data
• Current trends in linguistic research
• phonetic studies
• grammar & lexicon studies
• typological research
• sociolinguistic research
• psycholinguistics & language acquisition

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• Corpus data & Experimental data

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• Lots of practice in

Some case studies

probabilistic models of language

• Zipf’s law related research

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The correlation of frequency and range of words �in the spoken BNC (Gries to appear)

probabilistic models of language

• Kolmogorov, Prokhorov, Gasparov on rhyming in language

descriptive statistics, confirmatory

The distribution of different types of NPs across subject/non-subject slots (Aarts 1971: table 4.5, cited by Gries 2015:61)

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Subject slots prefer structurally lighter NPs: subjects are pronouns/names 86.2 percent of the time (5821/6749= 0.862) whereas non-subjects arepronouns/names 46 percent only of the time (2193/4770= 0.4,597); this is extremelyunlikely if there is no correlation between subjecthood and NP lightness.

multifactorial, confirmatory

• Chuang, Baayen et al. 2019 /

Geographical variation of the �merging between dental and

retroflex sibilants in Taiwan

Mandarin

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/s/ /ș/ /s/

/ts/ /tș/ /ts/

/ts^h/ /tș^h/ /ts^h/

Mandarin Min language geodata: geodata:� Min fluency Sibilant merging

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Types of variables

• numeric
• F0 (basic frequency in phonetics), responce time
• binary or categorical
• ordinal/scale (e.g. in the task to predict the etymological age of a verb (Baayen 2008))
• frequency counts (task to predict how frequently particular disfluencies happen in particular syntactic environments)

exploratory (hypothesis-generating)

• clustering

Janda &

Solovyev

2009

exploratory (hypothesis-generating)

• clustering

languages

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Hartmann, Haspelmath & Cysouw �(2014) Identifying semantic role �clusters and alignment types via �microrole coexpression tendencies

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exploratory (hypothesis-generating)

Dimensionality

reduction

exploratory (hypothesis-generating)

• Dimensions of variation in Biber (1988)

individual variation

mix-effect models

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Course Grades

• 40% -- course project

data collection (data), research design, descriptive statistics and data modeling, interpretation (written project paper), oral defence of the project (exam)

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• 60% -- homework assignments
• Rmd files submitted via GitHub (Classroom), DataCamp report
• deadline: before the next seminar
• late submission
• -10% less than a week later
• -70% any time before the exam

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Core Literature

• Gries, Stefan (2013). Statistics for Linguistics with R : A Practical Introduction (Vol. 2nd revised edition). Berlin: De Gruyter Mouton. eBook
• Levshina, Natalia (2015). How to Do Linguistics with R : Data Exploration and Statistical Analysis. Amsterdam: John Benjamins Publishing Company. eBook
• Baayen, Harald (2008). Analyzing Linguistic Data: A practical introduction to statistics. Cambridge UP. pdf

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• Gries, Stefan (2017). Quantitative Corpus Linguistics with R : A Practical Introduction (Vol. Second edition). Milton Park, Abingdon, Oxon: Routledge. eBook
• Hadley, W. (2016). Ggplot2 : Elegant Graphics for Data Analysis. Springer. eBook
• Harney, H. L. (2016). Bayesian Inference : Data Evaluation and Decisions (Vol. 2nd ed). Springer. eBook
• McElreath, R. (2016). Statistical Rethinking : A Bayesian Course with Examples in R and Stan. eBook