A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract)

Navid Nobani, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica

AAAI 2021 pp. 15859-15860

doi:10.1609/AAAI.V35I18.17926 /aaai/2021/nobani2021aaai-method/

Abstract

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.

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Cite

Text

Nobani et al. "A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17926

Markdown

[Nobani et al. "A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/nobani2021aaai-method/) doi:10.1609/AAAI.V35I18.17926

BibTeX

@inproceedings{nobani2021aaai-method,
  title     = {{A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract)}},
  author    = {Nobani, Navid and Malandri, Lorenzo and Mercorio, Fabio and Mezzanzanica, Mario},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15859-15860},
  doi       = {10.1609/AAAI.V35I18.17926},
  url       = {https://mlanthology.org/aaai/2021/nobani2021aaai-method/}
}