# Functional Distributional Semantics

arXiv: https://arxiv.org/abs/1606.08003 · HTML: https://mlsanity.com/paper/1606.08003/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [Does CLIP Bind Concepts? Probing Compositionality in Large Image Models](https://mlsanity.com/paper/2212.10537/) (2022)
- [Learning Functional Distributional Semantics with Visual Data](https://mlsanity.com/paper/2204.10624/) (2022)
- [Contextualized word senses: from attention to compositionality](https://mlsanity.com/paper/2312.00680/) (2023)

## Abstract (arXiv)

Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets.