RuDSI: graph-based word sense induction dataset for Russian
This provides a new benchmark for researchers working on word sense induction in Russian, though it is incremental as it builds on existing WSI concepts for a specific language.
The authors tackled the lack of a data-driven benchmark for word sense induction in Russian by creating RuDSI, a dataset based on manual annotation and semi-automatic clustering of Word Usage Graphs from the Russian National Corpus, and reported baseline performance scores for several methods.
We present RuDSI, a new benchmark for word sense induction (WSI) in Russian. The dataset was created using manual annotation and semi-automatic clustering of Word Usage Graphs (WUGs). Unlike prior WSI datasets for Russian, RuDSI is completely data-driven (based on texts from Russian National Corpus), with no external word senses imposed on annotators. Depending on the parameters of graph clustering, different derivative datasets can be produced from raw annotation. We report the performance that several baseline WSI methods obtain on RuDSI and discuss possibilities for improving these scores.