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.
This position paper focuses on the use of function words in computational authorship attribution. Although recently there have been multiple successful applications of authorship attribution, the field is not particularly good at the explication of methods and theoretical issues, which might eventually compromise the acceptance of new research results in the traditional humanities community. I wish to partially help remedy this lack of explication and theory, by contributing a theoretical discussion on the use of function words in stylometry. I will concisely survey the attractiveness of function words in stylometry and relate them to the use of character n-grams. At the end of this paper, I will propose to replace the term 'function word' by the term 'functor' in stylometry, due to multiple theoretical considerations.
In this paper we investigate whether Deep Convolutional Neural Networks (DCNNs), which have obtained state of the art results on the ImageNet challenge, are able to perform equally well on three different art classification problems. In particular, we assess whether it is beneficial to fine tune the networks instead of just using them as off the shelf feature extractors for a separately trained softmax classifier. Our experiments show how the first approach yields significantly better results and allows the DCNNs to develop new selective attention mechanisms over the images, which provide powerful insights about which pixel regions allow the networks successfully tackle the proposed classification challenges. Furthermore, we also show how DCNNs, which have been fine tuned on a large artistic collection, outperform the same architectures which are pre-trained on the ImageNet dataset only, when it comes to the classification of heritage objects from a different dataset.
In this paper we will stress-test a recently proposed technique for computational authorship verification, ''unmasking'', which has been well received in the literature. The technique envisages an experimental setup commonly referred to as ''authorship verification'', a task generally deemed more difficult than so-called ''authorship attribution''. We will apply the technique to authorship verification across genres, an extremely complex text categorization problem that so far has remained unexplored. We focus on five representative contemporary English-language authors. For each of them, the corpus under scrutiny contains several texts in two genres (literary prose and theatre plays). Our research confirms that unmasking is an interesting technique for computational authorship verification, especially yielding reliable results within the genre of (larger) prose works in our corpus. Authorship verification, however, proves much more difficult in the theatrical part of the corpus.
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