Data Mining Practical Machine Learning Tools and Techniques

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

Inference and Disputed Authorship: The Federalist

In the context of scholarly re-evaluations of James Joyce’s relation to the literary revival in Ireland at the start of the twentieth century, this essay examines the significance of W.B. Yeats to A Portrait of the Artist as a Young Man. It traces some of the debates around Celtic and Irish identity within the literary revival as a context for understanding the pre-occupations evident in Joyce’s novel, noting the significance of Yeats’s mysticism to the protagonist of Stephen Hero, and its persistence in A Portrait of the Artist as a Young Man later. The essay considers the theme of flight in relation to the poetry volume that is addressed directly in the novel, Yeats’s 1899 collection, The Wind Among the Reeds. In the process, the influence of Yeats’s thought and style is observed both in Stephen Dedalus’s forms of expression and in the means through which Joyce conveys them. Particular attention is drawn to the notion of enchantment in the novel, and its relation to the literature of the Irish Revival. The later part of the essay turns to the 1899 performance of Yeats’s play, The Countess Cathleen, at the Antient Concert Rooms in Dublin, and Joyce’s memory of the performance as represented through Stephen towards the end of the novel. Here, attention is given to the mystical and esoteric aspects of A Portrait of the Artist as a Young Man, aspects that the novel shares with the poetry and drama of Yeats.

Portrait of the Artist as a Young Man

1. Word Frequencies. 2. Non-parametric models. 3. Parametric models. 4. Mixture distributions. 5. The Randomness Assumption. 6. Examples of Applications. A. List of Symbols. B. Solutions of the exercises. C. Software. D. Data sets. Bibliography. Index.

Word Frequency Distributions

This software paper describes ‘Stylometry with R’ (stylo), a flexible R package for the highlevel analysis of writing style in stylometry. Stylometry (computational stylistics) is concerned with the quantitative study of writing style, e.g. authorship verification, an application which has considerable potential in forensic contexts, as well as historical research. In this paper we introduce the possibilities of stylo for computational text analysis, via a number of dummy case studies from English and French literature. We demonstrate how the package is particularly useful in the exploratory statistical analysis of texts, e.g. with respect to authorial writing style. Because stylo provides an attractive graphical user interface for high-level exploratory analyses, it is especially suited for an audience of novices, without programming skills (e.g. from the Digital Humanities). More experienced users can benefit from our implementation of a series of standard pipelines for text processing, as well as a number of similarity metrics.

/pdf/stylometry-with-r-a-package-for-computational-text-analysis-3b2dg44skx.pdf

Stylometry with R : a package for computational text analysis

The aim of this study is to find such a minimal size of text samples for authorship attribution that would provide stable results independent of random noise. A few controlled tests for different sample lengths, languages, and genres are discussed and compared. Depending on the corpus used, the minimal sample length varied from 2,500 words (Latin prose) to 5,000 or so words (in most cases, including English, German, Polish, and Hungarian novels). Another observation is connected with the method of sampling: contrary to common sense, randomly excerpted ‘bags of words’ turned out to be much more effective than the classical solution, i.e. using original sequences of words (‘passages’) of desired size. Although the tests have been performed using the Delta method ( Burrows, J.F . (2002). ‘Delta’: a measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing , 17 (3): 267–87) applied to the most frequent words, some additional experiments have been conducted for support vector machines and k -NN applied to most frequent words, character 3-grams, character 4-grams, and parts-of-speech-tag 3-grams. Despite significant differences in overall attributive success rate between particular methods and/or style markers, the minimal amount of textual data needed for reliable authorship attribution turned out to be method-independent.

Does Size Matter? Authorship Attribution, Small Samples, Big Problem.

Does Size Matter? Authorship Attribution, Small Samples, Big Problem

The aim of this article is to discuss reliability issues of a few visual techniques used in stylometry, and to introduce a new method that enhances the explanatory power of visualization with a procedure of validation inspired by advanced statistical methods A promising way of extending cluster analysis dendrograms with a self-validating procedure involves producing numerous particular ‘snapshots’, or dendrograms produced using different input parameters, and combining them all into the form of a consensus tree Significantly better results, however, can be obtained using a new visualization technique, which combines the idea of nearest neighborhood derived from cluster analysis, the idea of hammering out a clustering consensus from bootstrap consensus trees, with the idea of mapping textual similarities onto a form of a network Additionally, network analysis seems to be a good solution for large data sets

/pdf/visualization-in-stylometry-cluster-analysis-using-networks-334wkbo547.pdf

Visualization in Stylometry: Cluster Analysis Using Networks

http://dh2010.cch.kcl.ac.uk/academic-programme/abstracts/papers/pdf/ab-688.pdf

Maciej Eder

Papers

Stylometry with R : a package for computational text analysis

Does Size Matter? Authorship Attribution, Small Samples, Big Problem.

Does Size Matter? Authorship Attribution, Small Samples, Big Problem

Visualization in Stylometry: Cluster Analysis Using Networks

Deeper Delta across genres and languages: do we really need the most frequent words?