# Distributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functional Distributional Semantics

arXiv: https://arxiv.org/abs/2309.08325 · HTML: https://mlsanity.com/paper/2309.08325/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2023.

## Abstract (arXiv)

Functional Distributional Semantics (FDS) models the meaning of words by truth-conditional functions. This provides a natural representation for hypernymy but no guarantee that it can be learnt when FDS models are trained on a corpus. In this paper, we probe into FDS models and study the representations learnt, drawing connections between quantifications, the Distributional Inclusion Hypothesis (DIH), and the variational-autoencoding objective of FDS model training. Using synthetic data sets, we reveal that FDS models learn hypernymy on a restricted class of corpus that strictly follows the DIH. We further introduce a training objective that both enables hypernymy learning under the reverse of the DIH and improves hypernymy detection from real corpora.