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Semi-supervised Textual Analysis and Historical Research Helping Each Other: Some Thoughts and Observations

Semi-supervised Textual Analysis and Historical Research Helping Each Other: Some Thoughts and... <jats:p> 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. </jats:p><jats:p> 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. </jats:p><jats:p> 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. </jats:p> http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Humanities and Arts Computing Edinburgh University Press

Semi-supervised Textual Analysis and Historical Research Helping Each Other: Some Thoughts and Observations

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Publisher
Edinburgh University Press
Copyright
© Edinburgh University Press 2016
Subject
Special Issue: The Future of Digital Methods for Complex Datasets; Historical Studies
ISSN
1753-8548
eISSN
1755-1706
DOI
10.3366/ijhac.2016.0160
Publisher site
See Article on Publisher Site

Abstract

<jats:p> 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. </jats:p><jats:p> 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. </jats:p><jats:p> 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. </jats:p>

Journal

International Journal of Humanities and Arts ComputingEdinburgh University Press

Published: Mar 1, 2016

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