150,897 research outputs found
A Collaborative Ecosystem for Digital Coptic Studies
Scholarship on underresourced languages bring with them a variety of
challenges which make access to the full spectrum of source materials and their
evaluation difficult. For Coptic in particular, large scale analyses and any
kind of quantitative work become difficult due to the fragmentation of
manuscripts, the highly fusional nature of an incorporational morphology, and
the complications of dealing with influences from Hellenistic era Greek, among
other concerns. Many of these challenges, however, can be addressed using
Digital Humanities tools and standards. In this paper, we outline some of the
latest developments in Coptic Scriptorium, a DH project dedicated to bringing
Coptic resources online in uniform, machine readable, and openly available
formats. Collaborative web-based tools create online 'virtual departments' in
which scholars dispersed sparsely across the globe can collaborate, and natural
language processing tools counterbalance the scarcity of trained editors by
enabling machine processing of Coptic text to produce searchable, annotated
corpora.Comment: 9 pages; paper presented at the Stanford University CESTA Workshop
"Collecting, Preserving and Disseminating Endangered Cultural Heritage for
New Understandings Through Multilingual Approaches
Knowledge Organization Research in the last two decades: 1988-2008
We apply an automatic topic mapping system to records of publications in
knowledge organization published between 1988-2008. The data was collected from
journals publishing articles in the KO field from Web of Science database
(WoS). The results showed that while topics in the first decade (1988-1997)
were more traditional, the second decade (1998-2008) was marked by a more
technological orientation and by the appearance of more specialized topics
driven by the pervasiveness of the Web environment
Big data and the SP theory of intelligence
This article is about how the "SP theory of intelligence" and its realisation
in the "SP machine" may, with advantage, be applied to the management and
analysis of big data. The SP system -- introduced in the article and fully
described elsewhere -- may help to overcome the problem of variety in big data:
it has potential as "a universal framework for the representation and
processing of diverse kinds of knowledge" (UFK), helping to reduce the
diversity of formalisms and formats for knowledge and the different ways in
which they are processed. It has strengths in the unsupervised learning or
discovery of structure in data, in pattern recognition, in the parsing and
production of natural language, in several kinds of reasoning, and more. It
lends itself to the analysis of streaming data, helping to overcome the problem
of velocity in big data. Central in the workings of the system is lossless
compression of information: making big data smaller and reducing problems of
storage and management. There is potential for substantial economies in the
transmission of data, for big cuts in the use of energy in computing, for
faster processing, and for smaller and lighter computers. The system provides a
handle on the problem of veracity in big data, with potential to assist in the
management of errors and uncertainties in data. It lends itself to the
visualisation of knowledge structures and inferential processes. A
high-parallel, open-source version of the SP machine would provide a means for
researchers everywhere to explore what can be done with the system and to
create new versions of it.Comment: Accepted for publication in IEEE Acces
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