600 6 archive 91485 disk0/00/00/06/00 2026-03-30 14:56:58 2026-03-30 14:56:58 2026-03-30 14:56:58 dataset show Panchenko Alexander Ustalov Dmitry Faralli Stefano 0000-0003-3684-8815 Ponzetto Simone Paolo 0000-0001-7484-2049 Biemann Chris 004 30510 In this paper, we show for the first time how distributionally-induced semantic classes can be helpful for extraction of hypernyms. We present a method for (1) inducing sense-aware semantic classes using distributional semantics and (2) using these induced semantic classes for filtering noisy hypernymy relations. Denoising of hypernyms is performed by labeling each semantic class with its hypernyms. On one hand, this allows us to filter out wrong extractions using the global structure of the distributionally similar senses. On the other hand, we infer missing hypernyms via label propagation to cluster terms. We conduct a large-scale crowdsourcing study showing that processing of automatically extracted hypernyms using our approach improves the quality of the hypernymy extraction both in terms of precision and recall. Furthermore, we show the utility of our method in the domain taxonomy induction task, achieving the state-of-the-art results on a benchmarking dataset. This particular page contains datasets related to the paper. Namely the input induced word senses, a database of hypernyms, and the output clusters of senses labeled with hypernyms -- the distributional semantic classes. The semantic classes are of two granularities, as described in the paper (coarse and fine grained). eng 2018 10.7801/600 https://zenodo.org/records/1174041 metadata cc_by_sa_4 Panchenko, Alexander und Ustalov, Dmitry und Faralli, Stefano und Ponzetto, Simone Paolo und Biemann, Chris (2018), <a href='https://madoc.bib.uni-mannheim.de/id/eprint/43361' target='new'>Improving hypernymy extraction with distributional semantic classes</a> FALSE