Don’t blame distributional semantics if it can’t do entailment

Westera, Matthijs; Boleda, Gemma

Don’t blame distributional semantics if it can’t do entailment

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Citation

Westera M, Boleda G. Don’t blame distributional semantics if it can’t do entailment. In: Proceedings of the 13th International Conference on Computational Semantics - Long Papers (IWCS 2019); 2019 May 23-27; Gothenburg, Sweden. Stroudsburg (PA): ACL; 2019. p. 120-33.

Abstract

Distributional semantics has had enormous empirical success in Computational Linguistics and Cognitive Science in modeling various semantic phenomena, such as semantic similarity, and distributional models are widely used in state-of-the-art Natural Language Processing systems. However, the theoretical status of distributional semantics within a broader theory of language and cognition is still unclear: What does distributional semantics model? Can it be, on its own, a fully adequate model of the meanings of linguistic expressions? The standard answer is that distributional semantics is not fully adequate in this regard, because it falls short on some of the central aspects of formal semantic approaches: truth conditions, entailment, reference, and certain aspects of compositionality. We argue that this standard answer rests on a misconception: These aspects do not belong in a theory of expression meaning, they are instead aspects of speaker meaning, i.e., communicative intentions in a particular context. In a slogan: words do not refer, speakers do. Clearing this up enables us to argue that distributional semantics on its own is an adequate model of expression meaning. Our proposal sheds light on the role of distributional semantics in a broader theory of language and cognition, its relationship to formal semantics, and its place in computational models.

Description

Comunicació presentada a 13th International Conference on Computational Semantics (IWCS 2019), celebrat del 23 al 27 de maig de 2019 a Göteborg, Suècia.