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.

Nobani, N., Malandri, L., Mercorio, F., Mezzanzanica, M. (2021). A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract). In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (pp.15859-15860). 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA : Association for the Advancement of Artificial Intelligence.

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

Nobani N.;Malandri L.;Mercorio F.;Mezzanzanica M.
2021

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.
No
paper
word embedding, AI, taxonomy learning, machine learning;
English
35th AAAI Conference on Artificial Intelligence, AAAI 2021 - 2 February 2021 through 9 February 2021
978-171383597-4
Nobani, N., Malandri, L., Mercorio, F., Mezzanzanica, M. (2021). A Method for Taxonomy-Aware Embeddings Evaluation (Student Abstract). In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (pp.15859-15860). 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA : Association for the Advancement of Artificial Intelligence.
Nobani, N; Malandri, L; Mercorio, F; Mezzanzanica, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/385828
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