# Uncovering Probabilistic Implications in Typological Knowledge Bases

## TL;DR

This paper introduces a computational model that automatically identifies linguistic universals from typological knowledge bases, outperforming previous methods and uncovering both known and new universals for further linguistic research.

## Contribution

The paper presents a novel computational approach that effectively uncovers linguistic universals, including Greenberg universals, from typological data, reducing manual effort and expanding linguistic insights.

## Key findings

- Successfully identifies known linguistic universals
- Discovers new potential universals for linguistic study
- Outperforms existing baseline methods

## Abstract

The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions. Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which potentially leaves key linguistic universals unexplored. In this paper, we present a computational model which successfully identifies known universals, including Greenberg universals, but also uncovers new ones, worthy of further linguistic investigation. Our approach outperforms baselines previously used for this problem, as well as a strong baseline from knowledge base population.