Overcoming Copyright Barriers in Corpus Distribution Through Non-Reversible Hashing
While annotated corpora are crucial in the field of natural language processing (NLP), those containing copyrighted material are difficult to exchange among researchers. Yet, such corpora are necessary to fully represent the diversity of data found in the wild in the context of NLP tasks. We tackle this issue by proposing a method to lawfully and publicly share the annotations of copyrighted literary texts. The corpus creator shares the annotations in clear, along with a non-reversible hashed version of the source material. The corpus user must own the source material, and apply the same hash function to their own tokens, in order to match them to the shared annotations. Crucially, our me...
| Authors | Arthur Amalvy, Vincent Labatut, Xavier Bost et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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