SCoT: Sense Clustering over Time: a tool for the analysis of lexical change

Christian Haase, Saba Anwar, Seid Muhie Yimam, Alexander Friedrich, Chris Biemann


Abstract
We present Sense Clustering over Time (SCoT), a novel network-based tool for analysing lexical change. SCoT represents the meanings of a word as clusters of similar words. It visualises their formation, change, and demise. There are two main approaches to the exploration of dynamic networks: the discrete one compares a series of clustered graphs from separate points in time. The continuous one analyses the changes of one dynamic network over a time-span. SCoT offers a new hybrid solution. First, it aggregates time-stamped documents into intervals and calculates one sense graph per discrete interval. Then, it merges the static graphs to a new type of dynamic semantic neighbourhood graph over time. The resulting sense clusters offer uniquely detailed insights into lexical change over continuous intervals with model transparency and provenance. SCoT has been successfully used in a European study on the changing meaning of ‘crisis’.
Anthology ID:
2021.eacl-demos.23
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
198–204
Language:
URL:
https://aclanthology.org/2021.eacl-demos.23
DOI:
10.18653/v1/2021.eacl-demos.23
Bibkey:
Cite (ACL):
Christian Haase, Saba Anwar, Seid Muhie Yimam, Alexander Friedrich, and Chris Biemann. 2021. SCoT: Sense Clustering over Time: a tool for the analysis of lexical change. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 198–204, Online. Association for Computational Linguistics.
Cite (Informal):
SCoT: Sense Clustering over Time: a tool for the analysis of lexical change (Haase et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-demos.23.pdf