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Open problems in computational diversity linguistics
- Author(s):
- Johann-Mattis List (see profile)
- Date:
- 2019
- Group(s):
- Classical Philology and Linguistics, Digital Humanists, Digital Humanities East Asia, Linguistics
- Subject(s):
- Historical linguistics, Computational linguistics
- Item Type:
- Presentation
- Meeting Title:
- International Conference of Historical Linguisitcs
- Meeting Org.:
- Australian National University
- Meeting Loc.:
- Canberra
- Meeting Date:
- 2019-07-01/2019-07-05
- Tag(s):
- computer-assisted language comparison, open problems
- Permanent URL:
- http://dx.doi.org/10.17613/93t2-mt53
- Abstract:
- Despite a period of almost two decades in which quantitative approaches in historical linguistics have been increasingly used, gaining constantly more popularity even among predominantly qualitatively oriented linguists, we find many problems in the field of computational historical linguistics, which have only sporadically been addressed. In the talk, I will present a previously published list of 10 problems I personally deem important for historical linguistics, quickly explaining why I think that these problems are not solved yet, why they are hard to solve, but why I have confidence that they might be solved in the nearer or farer future. I will then present a computer-assisted framework for the development of problem-solving strategies in computational historical linguistics. The core of the framework is its interdisciplinary perspective, which helps to adapt existing solutions to similar problems in other disciplines to the problems in historical linguistics, while at the same time relying on a rigorous formalization and inspection of existing strategies in the "classical", qualitative approaches to historical language comparison. In contrast to the now very popular machine-learning approaches, which are widely applied to many problems in general and comparative linguistics, the framework is furthermore open with respect to the strategies which are used to solve a given problem, and it generally prefers explicit solutions (if they are available) over black-box strategies, as they are preferred in standard machine-learning frameworks. I will then pick four specific problems (automated morpheme detection, automated borrowing detection, automated induction of sound laws, and automated phonological reconstruction), and present initial strategies for their computer-assisted solution.
- Metadata:
- xml
- Status:
- Published
- Last Updated:
- 4 years ago
- License:
- Attribution-NonCommercial
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