Hierarchical and Non-Hierarchical Linear and Non-Linear Clustering Methods to “Shakespeare Authorship Question”

Abstract

A few literary scholars have long claimed that Shakespeare did not write some of his best plays (history plays and tragedies) and proposed at one time or another various suspect authorship candidates. Most modern-day scholars of Shakespeare have rejected this claim, arguing that strong evidence that Shakespeare wrote the plays and poems being his name appears on them as the author. This has caused and led to an ongoing scholarly academic debate for quite some long time. Stylometry is a fast-growing field often used to attribute authorship to anonymous or disputed texts. Stylometric attempts to resolve this literary puzzle have raised interesting questions over the past few years. The following paper contributes to "the Shakespeare authorship question" by using a mathematically-based methodology to examine the hypothesis that Shakespeare wrote all the disputed plays traditionally attributed to him. More specifically, the mathematically based methodology used here is based on Mean Proximity, as a linear hierarchical clustering method, and on Principal Components Analysis, as a non-hierarchical linear clustering method. It is also based, for the first time in the domain, on Self-Organizing Map U-Matrix and Voronoi Map, as non-linear clustering methods to cover the possibility that our data contains significant non-linearities. Vector Space Model (VSM) is used to convert texts into vectors in a high dimensional space. The aim of which is to compare the degrees of similarity within and between limited samples of text (the disputed plays). The various works and plays assumed to have been written by Shakespeare and possible authors notably, Sir Francis Bacon, Christopher Marlowe, John Fletcher, and Thomas Kyd, where "similarity" is defined in terms of correlation/distance coefficient measure based on the frequency of usage profiles of function words, word bi-grams, and character triple-grams. The claim that Shakespeare authored all the disputed plays traditionally attributed to him is falsified in favor of the alternative authors according to the stylistic criteria and analytic methodology used. The result

Lithuanian Author Profiling with the Deep Learning

“…The results for NBM and SVM were taken from [17]. NBM and SVM were tested with the different feature representation types: ultimate style markers, documentlevel character n-grams (with n= [2,7]), function words, token n-grams (with n= [1,3]), token lemmas (with n= [1,3]), part-ofspeech tag n-grams (with n= [1,3]), and n-grams of concatenated lexical and morphological features. There is no single the best feature representation type: it depends on the classification method and the dataset size (for the best types see Table I).…”

“…The majority of AP tasks are solved with the traditional machine learning methods and the weight vectors of features [3], [4]. The most influential examples of this field refer to Support Vector Machines (SVMs) [5], Multi-Class Real Winnow [6], Mean Proximity Clustering [7] and Holomorphic Transforms [8]. While a range of explored feature types usually covers stylistic (e.g., average sentence length, standardized type/token ratio), lexical (e.g., bag-of-words, function words), character (e.g., document or word-level character ngrams), morphological (e.g., part-of-speech tags) levels of feature representation types.…”

On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification

“…Author identification in the context of the literary genre attracted attention beyond the NLP research circles, e.g., due to the work by Aljumily (2015), who addressed the allegations that Shakespeare did not write some of his best plays using clustering techniques with function word frequency, word n-grams and character n-grams.…”

“…For both, feature engineering is crucial and for both the tendency is to use word/character n-grams and/or function and stop word frequencies (Mosteller and Wallace, 1963;Aljumily, 2015;Gamon, 2004;Argamon et al, 2009), PoS tags (Koppel et al, 2002;Mukherjee and Liu, 2010), or patterns captured by context-free-grammar-derived linguistic patterns; see e.g. (Raghavan et al, 2010;Sarawgi et al, 2011;Gamon, 2004).…”

Evaluation of the Performance and Efficiency of the Automated Linguistic Features for Author Identification in Short Text Messages Using Different Variable Selection Techniques

“…This assumption is very well-known and well-understood and explained, and there is an extremely long line of research on this subject (e.g. Aljumily, 2015;2017;Holmes, 1994Holmes, , 1998Holmes and Kardos, 2003). More details about the linguistic features for authorship attribution that have been proposed in the literature is discussed in, for example, Stamatatos (2009).…”