# Learning Word Embeddings for Hyponymy with Entailment-Based Distributional Semantics

arXiv: https://arxiv.org/abs/1710.02437 · HTML: https://mlsanity.com/paper/1710.02437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Yseop at FinSim-3 Shared Task 2021: Specializing Financial Domain Learning with Phrase Representations](https://mlsanity.com/paper/2108.09485/) (2021)

## Abstract (arXiv)

Lexical entailment, such as hyponymy, is a fundamental issue in the semantics of natural language. This paper proposes distributional semantic models which efficiently learn word embeddings for entailment, using a recently-proposed framework for modelling entailment in a vector-space. These models postulate a latent vector for a pseudo-phrase containing two neighbouring word vectors. We investigate both modelling words as the evidence they contribute about this phrase vector, or as the posterior distribution of a one-word phrase vector, and find that the posterior vectors perform better. The resulting word embeddings outperform the best previous results on predicting hyponymy between words, in unsupervised and semi-supervised experiments.