Graded hyponymy for compositional distributional semantics

The categorical compositional distributional model of natural language provides a conceptually motivated procedure to compute the meaning of a sentence, given its grammatical structure and the meanings of its words. This approach has outperformed other models in mainstream empirical language processing tasks, but lacks an effective model of lexical entailment. We address this shortcoming by exploiting the freedom in our abstract categorical framework to change our choice of semantic model. This allows us to describe hyponymy as a graded order on meanings, using models of partial information used in quantum computation. Quantum logic embeds in this graded order.

• Publication year

  2019

• Venue

  Journal of Language Modelling

• Publication date

  2019-03-06

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.15398/JLM.V6I2.230
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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