# Don't Blame Distributional Semantics if it can't do Entailment

## TL;DR

This paper argues that distributional semantics effectively models expression meaning, challenging the view that it must account for truth conditions and entailment, which are aspects of speaker meaning and context.

## Contribution

It redefines the scope of distributional semantics, positioning it as an adequate model of expression meaning rather than speaker meaning, clarifying its role in language theory.

## Key findings

- Distributional semantics models expression meaning effectively.
- Entailment and truth conditions are aspects of speaker meaning, not expression meaning.
- Reconceptualizing distributional semantics clarifies its role in language and cognition.

## 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.