A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v35i18.17926Keywords:
Embedding-evaluation, Word Embeddings, Semantic WebAbstract
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.Downloads
Published
2021-05-18
How to Cite
Nobani, N., Malandri, L., Mercorio, F., & Mezzanzanica, M. (2021). A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15859-15860. https://doi.org/10.1609/aaai.v35i18.17926
Issue
Section
AAAI Student Abstract and Poster Program