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In this paper we illustrate a system aimed at solving a longstanding and challenging problem: acquiring a classifier to automatically annotate bibliographic records by starting from a huge set of unbalanced and unlabelled data. We illustrate the main features of the dataset, the learning algorithm adopted, and how it was used to discriminate philosophical documents from documents of other disciplines. One strength of our approach lies in the novel combination of a standard learning approach with a semantic one: the results of the acquired classifier are improved by accessing a semantic network containing conceptual information. We illustrate the experimentation by describing the construction rationale of training and test set, we report and discuss the obtained results and conclude by drawing future work.

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There is a rather widespread consensus, among historians of philosophy, concerning the decline of Wittgenstein amid recent analytic philosophy. However, the exact import of such a decline, its chronological development, as well as its causes and several other features, are difficult to ascertain with the traditional methods of the history of philosophy. In this article we applied a distant reading approach, and a variety of other quantitative methods, trying to provide a more reliable and accurate account of Wittgenstein's decline. We focused on a corpus consisting of the metadata of US PhD dissertations in philosophy from 1981 to 2010 (although other kinds of data are also taken into consideration), and we tried to relate the topic of the dissertation with the success of the candidate in his/her subsequent academic career. The results of this analysis, corroborated by other evidence, allowed us to put forth the more reliable and accurate account just hinted at, and at the same time to suggest-as a contribution to external history of philosophy-a plausible mechanism at the basis of the decline itself, notably a process driven by those who controlled the recruitment policies in the philosophy departments.