| Autor: |
Nobani N., Malandri L., Mercorio F., Mezzanzanica M. |
| Přispěvatelé: |
Nobani, N, Malandri, L, Mercorio, F, Mezzanzanica, M |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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