Browsing by Author "Murdock, Jaimie"

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    Hypershelf: A Multidimensional Browser for Exploring Content Across the Library
    (Indiana University Digital Collections Services, 2016-08-31) Allen, Colin; Murdock, Jaimie
    Research libraries continue to reinvent themselves in the face of increasing demand from users for digitized texts. As physical books move from stacks to deep storage, many researchers lament the reduction in the serendipitous discovery that was provided by browsing the stacks. We believe, however, that digitization offers even greater opportunities for guided serendipity. Developments in machine learning and computing at scale allow content-based models of library collections to be made accessible to patrons. In this talk, we will present a vision for the future of library browsing using the Topic Explorer “Hypershelf” that we have developed for digital collections. It allows users to jump into the collection and browse nearby volumes, rearranging them at will according to topics extracted computationally from the full texts. We will demonstrate the Hypershelf in action, and discuss how it might be integrated with physically-shelved books. This vision enhances rather than supplants the traditional librarians’ function of guiding patrons to the best starting points for their research needs.
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    Supporting Data for "Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library"
    (2017) Rose, Robert; Rose, Doori; Otsuka, Jun; Murdock, Jaimie; Allen, Colin
    We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classification systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of ``drill-down'' topic modeling---simultaneously reducing both the size of the corpus and the unit of analysis---we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historians and philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of "close reading" needed to generate original interpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances understanding of the intellectual and societal contexts in which writings are interpreted.