@article{Nobani_Malandri_Mercorio_Mezzanzanica_2021, title={A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract)}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17926}, DOI={10.1609/aaai.v35i18.17926}, abstractNote={While word embeddings have been showing their effectiveness in capturing semantic and lexical similarities in a large number of domains, in case the corpus used to generate embeddings is associated with a taxonomy (i.e., classification tasks over standard de-jure taxonomies) the common intrinsic and extrinsic evaluation tasks cannot guarantee that the generated embeddings are consistent with the taxonomy. This, as a consequence sharply limits the use of distributional semantics in those domains. To address this issue, we design and implement MEET, which proposes a new measure -HSS- that allows evaluating embeddings from a text corpus preserving the semantic similarity relations of the taxonomy.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Nobani, Navid and Malandri, Lorenzo and Mercorio, Fabio and Mezzanzanica, Mario}, year={2021}, month={May}, pages={15859-15860} }