The optical and scopic urges viewed from a phenomenological perspective

Taking as starting points the books The Address of the Eye: A Phenomenology of Film Experience, by Vivian Sobchak, and Les quatre concepts fondamentaux de la psychanalyse, by Jacques Lacan, this article proposes to look at two well renowned film objects – Rear Window (Alfred Hitchcock, 1954, USA) and Peeping Tom (Michael Powell, 1960, UK) – in order to equate two forms of perception that, all things considered, come together as one: the perception of the mechanical apparatuses that record and project film and the optical and mental apparatuses that operate on the human filmmakers as well as their intradiegetic protagonists. In fact, these two films not only explore the characteristics and limits of vision and affection in their diegetic world, that is part of the filmmaker’s world itself, but reveals just how much the human lives through the eye and the expression of the machine itself. Film ontology is foremost a matter of (re)production rather than creation.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.