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

The increasing number of experimental studies on second language (L2) processing, frequently with English as the L2, calls for a practical and valid measure of English vocabulary knowledge and proficiency. In a large-scale study with Dutch and Korean speakers of L2 English, we tested whether LexTALE, a 5-min vocabulary test, is a valid predictor of English vocabulary knowledge and, possibly, even of general English proficiency. Furthermore, the validity of LexTALE was compared with that of self-ratings of proficiency, a measure frequently used by L2 researchers. The results showed the following in both speaker groups: (1) LexTALE was a good predictor of English vocabulary knowledge; 2) it also correlated substantially with a measure of general English proficiency; and 3) LexTALE was generally superior to self-ratings in its predictions. LexTALE, but not self-ratings, also correlated highly with previous experimental data on two word recognition paradigms. The test can be carried out on or downloaded from www.lextale.com.

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Introducing LexTALE: A quick and valid Lexical Test for Advanced Learners of English

In spite of the intuition of many bilinguals, a review of empirical studies indicates that during reading under many circumstances, possible words from different languages temporarily become active. Such evidence for "language non-selective lexical access" is found using stimulus materials of various kinds: interlingual homographs (words that are identical in orthography between languages, such as the English-Dutch word BRAND, meaning "fire" in Dutch), cognates (words that have an orthography and a meaning that are similar or identical across languages, such as TOMATO in English and TOMAAT in Dutch), and interlingual neighbors (words from two languages that differ in only one letter position, such as STEAK and STERK, meaning "strong" in Dutch). However, although there is parallel lexical activation in both languages during bilingual word recognition, the actually observed result patterns also appear to be task-dependent. A distinction must therefore be made between factors affecting the word identification system directly (such as sentence context) and factors affecting the task/decision system (non-linguistic context and task demands). Recent models of bilingual word recognition are discussed with respect to these two types of factors.

Bilingual visual word recognition and lexical access

Foreword Note on graphic conventions 0. Introduction-Toward a Logic of Culture 0.1. Design for a semiotic theory 0.2. 'Semiotics': field or discipline? 0.3. Communication and/or signification 0.4. Political boundaries: the field 0.5. Natural boundaries: two definitions of semiotics 0.6. Natural boundaries: inference and signification 0.7. Natural boundaries the lower threshold 0.8. Natural boundaries: the upper threshold 0.9. Epistemological boundaries 1. Signification and Communication 1.1. An elementary communicational model 1.2. Systems and codes 1.3. The s-code as structure 1.4. Information, communication, signification 2. Theory of Codes 2.1. The sign-function 2.2. Expression and content 2.3. Denotation and connotation 2.4. Message and text 2.5 Content and referent 2.6. Meaning as cultural unit 2.7. The interpretant 2.8. The semantic system 2.9. The semantic markers and the sememe 2.10. The KF model 2.11. A revised semantic model 2.12. The model \"Q\" 2.13. The format of the semantic space 2.14. Overcoding and undercoding 2.15. The interplay of codes and the message as an open form 3. Theory of Sign Production 3.1. A general survey 3.2. Semiotic and factual statements 3.3. Mentioning 3.4 The prolem of a typology of signs 3.5. Critique of iconism 3.6. A typology of modes of production 3.7. The aesthetic text as invention 3.8. The rhetorical labor 3.9. Ideological code switching 4. The Subject of Semiotics References Index of authors Index of subjects

A Theory of Semiotics

Normal aging is an inevitable race between increasing knowledge and decreasing cognitive capacity. Crucial to understanding and promoting successful aging is determining which of these factors dominates for particular neurocognitive functions. Here, we focus on the human capacity for language, for which healthy older adults are simultaneously advantaged and disadvantaged. In recent years, a more hopeful view of cognitive aging has emerged from work suggesting that age-related declines in executive control functions are buffered by life-long bilingualism. In this paper, we selectively review what is currently known and unknown about bilingualism, executive control, and aging. Our ultimate goal is to advance the views that these issues should be reframed as a specific instance of neuroplasticity more generally and, in particular, that researchers should embrace the individual variability among bilinguals by adopting experimental and statistical approaches that respect the complexity of the questions addressed. In what follows, we set out the theoretical assumptions and empirical support of the bilingual advantages perspective, review what we know about language, cognitive control, and aging generally, and then highlight several of the relatively few studies that have investigated bilingual language processing in older adults, either on their own or in comparison with monolingual older adults. We conclude with several recommendations for how the field ought to proceed to achieve a more multifactorial view of bilingualism that emphasizes the notion of neuroplasticity over that of simple bilingual versus monolingual group comparisons.

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Moving toward a neuroplasticity view of bilingualism, executive control, and aging

How cross-language similarity and task demands affect cognate recognition

This paper describes the new multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed typical knowledge work (writing reports, making presentations, reading e-mail, searching for information). We manipulated their working conditions with the stressors: email interruptions and time pressure. A varied set of data was recorded: computer logging, facial expression from camera recordings, body postures from a Kinect 3D sensor and heart rate (variability) and skin conductance from body sensors. The dataset made available not only contains raw data, but also preprocessed data and extracted features. The participants' subjective experience on task load, mental effort, emotion and perceived stress was assessed with validated questionnaires as a ground truth. The resulting dataset on working behavior and affect is a valuable contribution to several research fields, such as work psychology, user modeling and context aware systems.

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The SWELL Knowledge Work Dataset for Stress and User Modeling Research

We evaluate five term scoring methods for automatic term extraction on four different types of text collections: personal document collections, news articles, scientific articles and medical discharge summaries. Each collection has its own use case: author profiling, boolean query term suggestion, personalized query suggestion and patient query expansion. The methods for term scoring that have been proposed in the literature were designed with a specific goal in mind. However, it is as yet unclear how these methods perform on collections with characteristics different than what they were designed for, and which method is the most suitable for a given (new) collection. In a series of experiments, we evaluate, compare and analyse the output of six term scoring methods for the collections at hand. We found that the most important factors in the success of a term scoring method are the size of the collection and the importance of multi-word terms in the domain. Larger collections lead to better terms; all methods are hindered by small collection sizes (below 1000 words). The most flexible method for the extraction of single-word and multi-word terms is pointwise Kullback---Leibler divergence for informativeness and phraseness. Overall, we have shown that extracting relevant terms using unsupervised term scoring methods is possible in diverse use cases, and that the methods are applicable in more contexts than their original design purpose.

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Evaluation and analysis of term scoring methods for term extraction

Assessing e-mail intent and tasks in e-mail messages

In this paper, we evaluate query term suggestion in the context of academic professional search. Our overall goal is to support scientists in their information seeking tasks. We set up an interactive search system in which terms are extracted from clicked documents and suggested to the user before every query specification step. We evaluated our method with the iSearch collection of academic information seeking behaviour and crowdsourced term relevance judgements. We found that query term suggestion can significantly improve recall in academic search.

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Maya Sappelli

Papers

How cross-language similarity and task demands affect cognate recognition

The SWELL Knowledge Work Dataset for Stress and User Modeling Research

Evaluation and analysis of term scoring methods for term extraction

Assessing e-mail intent and tasks in e-mail messages

Query Term Suggestion in Academic Search