http://talklab.psy.gla.ac.uk/KeepItMaximalR2.pdf

Random effects structure for confirmatory hypothesis testing: Keep it maximal

The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

Ethnologue: Languages of the World

This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.’s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions.

/pdf/neural-network-acceptability-judgments-29tbzxmqj1.pdf

Neural Network Acceptability Judgments

The World Atlas of Language Structures Online

To test this hypothesis, Atkinson employs a sample of 504 non-extinct languages from WALS (Haspelmath et al. (eds.) 2008), for which the number of vowels, the number of consonants, and the number of tones in the language are annotated (Maddieson 2008a, b, c). For the main analysis, these three measures were standardized (i.e., the mean was subtracted from each value, which was then divided by the standard deviation of the measure) and averaged into one combined measure of the total phonological diversity of a language. This normalized phonological diversity measure ranges from −1.19 to 1.68 (mean = 0.02). Each language is also annotated for its coordinates on the globe as well as it population size (the number of speakers). The main text of Atkinson 2011 presents the results of a linear regression analysis of normalized phonological diversity against the distance from the hypothesized “origin of language” while controlling for log-transformed population size and its interaction with the distance from the origin (population size data was taken from Gordon & Grimes (eds.) 2005). The origin of language is determined by comparing the model fit for all 2,560 language coordinates found in the version of WALS employed by AUTHOR’S COPY | AUTORENEXEMPLAR

/pdf/mixed-effect-models-for-genetic-and-areal-dependencies-in-56znuv6z5a.pdf

Mixed effect models for genetic and areal dependencies in linguistic typology

How flexible is an individual’s accent during adulthood, and how does this flexibility relate to longer-term change? Previous work has found that accents are remarkably flexible in conversational interaction, but predominantly stable over years, leading to very different views of the role of individuals in community-level sound change. This article examines medium-term accent dynamics (days to months) by taking advantage of a ‘natural experiment’: a reality television show where contestants live in an isolated house for three months and are constantly recorded, forming a closed system where it is possible to both determine the dynamics of contestants’ speech from day to day and reason about the sources of any observed changes. We build statistical models to examine time dependence in five phonetic variables within individuals, in 14.5 hours of spontaneous speech from twelve English-speaking contestants. We find that time dependence in pronunciation is ubiquitous over the medium term: large daily fluctuations in pronunciation are the norm, while longer-term change over weeks to months occurs in a minority of cases. These patterns mirror the conflicting findings of previous work and suggest a possible bridge between the two. We argue that time dependence in phonetic variables is influenced by contrast between sounds, as well as systematic differences between speakers in how malleable their accents are over time; however, we find only limited evidence for convergence in individuals’ accents. Our results have implications for theories of the role of individuals in sound change, and suggest that medium-term pronunciation dynamics are a fruitful direction for future work.

The medium-term dynamics of accents on reality television

https://scholar.harvard.edu/files/mpolinsky/files/lingua_offprint.pdf

Subject preference and ergativity

While published linguistic judgments sometimes differ from the judgments found in large-scale formal experiments with naive participants, there is not a consensus as to how often these errors occur nor as to how often formal experiments should be used in syntax and semantics research. In this article, we first present the results of a large-scale replication of the Sprouse et al. 2013 study on 100 English contrasts randomly sampled from Linguistic Inquiry 2001–2010 and tested in both a forced-choice experiment and an acceptability rating experiment. Like Sprouse, Schutze, and Almeida, we find that the effect sizes of published linguistic acceptability judgments are not uniformly large or consistent but rather form a continuum from very large effects to small or nonexistent effects. We then use this data as a prior in a Bayesian framework to propose a small n acceptability paradigm for linguistic acceptability judgments (SNAP Judgments). This proposal makes it easier and cheaper to obtain meaningful quantitative data in syntax and semantics research. Specifically, for a contrast of linguistic interest for which a researcher is confident that sentence A is better than sentence B, we recommend that the researcher should obtain judgments from at least five unique participants, using at least five unique sentences of each type. If all participants in the sample agree that sentence A is better than sentence B, then the researcher can be confident that the result of a full forced-choice experiment would likely be 75% or more agreement in favor of sentence A (with a mean of 93%). We test this proposal by sampling from the existing data and find that it gives reliable performance.

/pdf/snap-judgments-a-small-n-acceptability-paradigm-snap-for-wv05vd7nkm.pdf

SNAP judgments: A small N acceptability paradigm (SNAP) for linguistic acceptability judgments

/pdf/subject-islands-are-different-1qi67i8a5t.pdf

Peter Graff

Papers

Mixed effect models for genetic and areal dependencies in linguistic typology

The medium-term dynamics of accents on reality television

Subject preference and ergativity

SNAP judgments: A small N acceptability paradigm (SNAP) for linguistic acceptability judgments

Subject islands are different