The CrossTimeLines package including the source code, scorers, gold-standard and corpus can be downloaded here: [tar.gz]
License
This package is distributed under Attribution 3.0 Unported (CC BY 3.0) license. You can find it at http://creativecommons.org/licenses/by/3.0.
Publications
Laparra E., Aldabe I., Agerri R. and Rigau G. Multi-lingual and Cross-lingual TimeLine Extraction. Knowledge-Based Systems, Volume 133, pages 77-89, ISSN 0950-7051, 2017. https://doi.org/10.1016/j.knosys.2017.07.002.
A2T Domains (A2TD) (Sainz and Rigau 2021) is a lexical resource generated as part of the Ask2Transformers work. It consists of WordNet synsets automatically annotated with domain information, such us BabelDomains labels.
The Ask2Transformers work aims to automatically annotate textual data without any supervision. Given a particular set of labels (BabelDomains, WNDomains, ...), the system have to classify the data without previous examples. This work is based on Transformers library and it's pretrained LMs. For this particular resource we evaluated the systems on BabelDomains dataset (Camacho-Collados and Navigli, 2017) achieving 92.14 of accuracy on domain labelling.
You can find the code of the Ask2Transformers work on Github: https://github.com/osainz59/Ask2Transformers
For further information about the A2T Domains please check this README.
Downloads
A2T Domains: [tar.gz]
License
This package is distributed under Attribution 3.0 Unported (CC BY 3.0) license. You can find it at http://creativecommons.org/licenses/by/3.0.
Publications
Sainz O. and Rigau G. Ask2Transformers: Zero-Shot Domain labelling with Pre-trained Language Models. Proceedings of the 11th Global WordNet Conference (GWC 2021). Pretoria, South Africa. 2021.
References
Camacho-Collados, Jose, and Roberto Navigli. "BabelDomains: Large-scale domain labeling of lexical resources." In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 223-228. 2017.