new-data-spaces

Best Demo Award at NAACL 2025

We are delighted that our paper “Towards Unified, Dynamic, and Annotation-based Visualizations and Exploration of Annotated Big Data Corpora with the Help of Unified Corpus Explorer” has been awarded the Best Demo Paper at this year’s annual conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL 2025).

Kevin Bönisch, Giuseppe Abrami and Alexander Mehler. 2025. Towards Unified, Dynamic and Annotation-based Visualisations and Exploration of Annotated Big Data Corpora with the Help of Unified Corpus Explorer. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations), 522–534. Best Demo Award.
BibTeX
@inproceedings{Boenisch:et:al:2025,
  title     = {Towards Unified, Dynamic and Annotation-based Visualisations and
               Exploration of Annotated Big Data Corpora with the Help of Unified
               Corpus Explorer},
  author    = {B{\"o}nisch, Kevin and Abrami, Giuseppe and Mehler, Alexander},
  editor    = {Dziri, Nouha and Ren, Sean (Xiang) and Diao, Shizhe},
  booktitle = {Proceedings of the 2025 Conference of the Nations of the Americas
               Chapter of the Association for Computational Linguistics: Human
               Language Technologies (System Demonstrations)},
  year      = {2025},
  address   = {Albuquerque, New Mexico},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2025.naacl-demo.42/},
  pages     = {522--534},
  isbn      = {979-8-89176-191-9},
  abstract  = {The annotation and exploration of large text corpora, both automatic
               and manual, presents significant challenges across multiple disciplines,
               including linguistics, digital humanities, biology, and legal
               science. These challenges are exacerbated by the heterogeneity
               of processing methods, which complicates corpus visualization,
               interaction, and integration. To address these issues, we introduce
               the Unified Corpus Explorer (UCE), a standardized, dockerized,
               open-source and dynamic Natural Language Processing (NLP) application
               designed for flexible and scalable corpus navigation. Herein,
               UCE utilizes the UIMA format for NLP annotations as a standardized
               input, constructing interfaces and features around those annotations
               while dynamically adapting to the corpora and their extracted
               annotations. We evaluate UCE based on a user study and demonstrate
               its versatility as a corpus explorer based on generative AI.},
  note      = {Best Demo Award},
  pdf       = {https://aclanthology.org/2025.naacl-demo.42.pdf},
  keywords  = {uce,new-data-spaces,circlet,core,core_c08}
}