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Matrix In a previous tutorial article I looked at a proximity coefficient and, in the light of that proximity created a vector-distance matrix and used it to construct a hierarchical tree using different hierarchical clustering methods... more
Matrix In a previous tutorial article I looked at a proximity coefficient and, in the light of that proximity created a vector-distance matrix and used it to construct a hierarchical tree using different hierarchical clustering methods which will be the basis for exploratory multivariate analysis. The present article deals with three topics: (i) standardization for variable scales variation, (ii) normalization for sample length variation, and (iii) dimensionality reduction or minimization of data space. These techniques reflect the author's academic background and particular area of interest and are, by necessity, not a particular purpose and are straightforwardly applicable to other kinds of data, and thus to a wide range of analysis in Linguistics. My treatment of these techniques is, necessarily, introductory and brief. I hope that this article will provide practitioners with an introductory overview of these techniques used for cluster analysis of electronic corpora of linguistic data. The assumption is that the data is in the form of an m x n matrix D in which, may require to transform it in various ways prior to cluster analyzing it. Standardized data matrix enables practitioners to measure the variation between n-variables and to cluster the cases they describe in common scales and values, regardless of their original scales and values. Normalized data matrix enables practitioners to eliminate the effect of variation in length among n-samples and to cluster them as if they were all (about) the same length, regardless of their original length. Dimensionality-reduced space data matrix enables practitioners to select and/or extract n-most interesting variables relevant to the research question and to visualize an existing pattern, regardless of the original space. A worked example is given to illustrate the effect each transformation technique has on a given data matrix. These transformation techniques have their own strengths and weakness but are beyond the scope of my objectives in this
The scholars, Frederick Burwick and James McKusick, published at Oxford University Press, Faustus from the German of Goethe translated by Samuel Taylor Coleridge in 2007. This edition articulated the result that Samuel Taylor Coleridge is... more
The scholars, Frederick Burwick and James McKusick, published at Oxford University Press, Faustus from the German of Goethe translated by Samuel Taylor Coleridge in 2007. This edition articulated the result that Samuel Taylor Coleridge is the actual translator of the anonymously published translation Faustus from the German of Goethe (London: Boosey: 1821). The present article tests that result. The approach to test this result is stylometric. Specifically, function word usage is selected as the stylometric criterion, and 80 function words are used to define a 73-dimensional function word frequency profile vector for each text in the corpus of Coleridge's literary works and for a selection of works by a range of contemporary English authors. Each profile vector is a point in 80-dimensional vector space, and 5 different cluster analytic methods are used to determine the distribution of profile vectors in the space. If the result being tested is valid, then the profile for the 1821 translation should be closer in the space to works known to be by Coleridge than to works by the other authors. The cluster analytic results show, however, that this is not the case, and the conclusion is that the Burwick and McKusick result is falsified relative to the stylometric criterion and analytic methodology used. Where, in Popperian terms, falsification does not mean 'prove to be false'. It means that evidence which contradicts a hypothesis has been presented, and it is up to the proposer of the hypothesis either to show that the evidence is inadmissible or irrelevant, or else to emend the hypothesis accordingly
The rest of the article is organized as follows. In section 1 we give the motivation for doing this work. In section 2 we provide a quick introduction to the 1821 Faustus translations that we hope will shed some light on the problem. In section 3 we discuss the previous attempts to attribute the 1821 Faustus to Coleridge. In section 4 we outline the methodology used to address the 1821 Faust translation authorship debate. In section 5 we present data preparation. In section 6 we present our main analytical arguments deriving the evidence to refute Coleriadge’a authorship of Faustus. We also present the clustering results obtained in section 6. In section 7 we provide additional interpretation for the analytical results obtained in section 6. We conclude in section 8 with a summary of our results, and discussing open questions and possible future directions.
The rest of the article is organized as follows. In section 1 we give the motivation for doing this work. In section 2 we provide a quick introduction to the 1821 Faustus translations that we hope will shed some light on the problem. In section 3 we discuss the previous attempts to attribute the 1821 Faustus to Coleridge. In section 4 we outline the methodology used to address the 1821 Faust translation authorship debate. In section 5 we present data preparation. In section 6 we present our main analytical arguments deriving the evidence to refute Coleriadge’a authorship of Faustus. We also present the clustering results obtained in section 6. In section 7 we provide additional interpretation for the analytical results obtained in section 6. We conclude in section 8 with a summary of our results, and discussing open questions and possible future directions.