@inproceedings{KurtyigitParkSchlechtwegetal.2021,
  author    = {Sinan Kurtyigit and Maike Park and Dominik Schlechtweg and Jonas Kuhn and Sabine Schulte im Walde},
  title     = {Lexical semantic change discovery},
  series = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  editor    = {Chengqing Zong and Fei Xia and Wenjie Li and Roberto Navigli},
  publisher = {Association for Computational Linguistics},
  address   = {Stroudsburg},
  isbn      = {978-1-954085-52-7},
  doi       = {10.18653/v1/2021.acl-long.543},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-106930},
  pages     = {6985 -- 6998},
  year      = {2021},
  abstract  = {While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated framework for both evaluation and discovery.},
  language  = {en}
}