SCoT (Sense Clustering over Time) is a web application to view the sense-clusters of a word and their evolvement over time. The idea is to help scholars and scientists interested in diachronic distributional semantics visualize and analyse the clusters on a graph.
You can find SCoT at http://ltdemos.informatik.uni-hamburg.de/scot/
The functionalities and usage are described in the user guide.
If you would like to deploy or develop SCoT yourself, see installation guide.
The project is maintained by the Language Technology Group at Universität Hamburg.
Developers:
- Christian Haase [Version 2]
- Inga Kempfert [Version 1]
- Saba Anwar
Supervision:
References:
- Kempfert, I., Anwar, S., Friedrich, A., Biemann, C. (2020): Digital History of Concepts: Sense Clustering over Time. 42. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft (DGfS), Hamburg, Germany. (abstract pdf)
- Friedrich, A. and Biemann, C. (2016): Digitale Begriffsgeschichte? Methodologische Überlegungen und exemplarische Versuche am Beispiel moderner Netzsemantik, in: Forum Interdisziplinäre Begriffsgeschichte 5(2):78-96 (pdf)
- Riedl, Martin, Steuer, Richard, Biemann, Chris (2014): Distributed Distributional Similarities of Google Books over the Centuries, in: Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 14), pp. 1401-1405, Reykjavik, Iceland. (pdf)
- Biemann, Chris (2006): Chinese Whispers - an Efficient Graph Clustering Algorithm and its Application to Natural Language Processing Problems. In: Association for Computational Linguistics (Hg.): TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing. Stroudsburg, S. 73–80.