Semi-supervised Textual Analysis and Historical Research Helping Each Other: Some Thoughts and Observations. Issue 1 (March 2016)

• Record Type: Journal Article
• Authors: Nanni, Federico; Kümper, Hiram; Ponzetto, Simone Paolo
• Is Part Of: International journal of humanities and arts computing. Volume 10:Issue 1(2016)
• Page Start: 63
• Page End: 77
• Publication Date: 2016-03
• Subjects: semi-supervised methods -- historical studies -- data analysis -- born-digital archives; Humanities -- Data processing -- Periodicals; Arts -- Data processing -- Periodicals; 001.30285
• DOI: 10.3366/ijhac.2016.0160
• Languages: English
• ISSNs: 1753-8548

Abstract

Abstract : Future historians will describe the rise of the World Wide Web as the turning point of their academic profession. As a matter of fact, thanks to an unprecedented amount of digitization projects and to the preservation of born-digital sources, for the first time they have at their disposal a gigantic collection of traces of our past. However, to understand trends and obtain useful insights from these very large amounts of data, historians will need more and more fine-grained techniques. This will be especially true if their objective will turn to hypothesis-testing studies, in order to build arguments by employing their deep in-domain expertise. For this reason, we focus our paper on a set of computational techniques, namely semi-supervised computational methods, which could potentially provide us with a methodological turning point for this change. As a matter of fact these approaches, due to their potential of affirming themselves as both knowledge and data driven at the same time, could become a solid alternative to some of the today most employed unsupervised techniques. However, historians who intend to employ them as evidences for supporting a claim, have to use computational methods not anymore as black boxes but as a series of well known methodological approaches. For this reason, we believe that if developing computational skills will be important for them, a solid background knowledge on the most important data analysis and results evaluation procedures will become far more capital.