# A Survey of Distant Supervision Methods using PGMs

## TL;DR

This survey reviews various distant supervision techniques for relation extraction that primarily utilize Probabilistic Graphical Models, highlighting their methodologies and applications in automating training data generation from knowledge bases.

## Contribution

It provides a comprehensive overview of PGM-based distant supervision methods, summarizing their approaches, advantages, and challenges in relation extraction tasks.

## Key findings

- PGMs effectively model uncertainties in distant supervision.
- Various PGM-based methods improve relation extraction accuracy.
- The survey identifies open challenges and future directions.

## Abstract

Relation Extraction refers to the task of populating a database with tuples of the form $r(e_1, e_2)$, where $r$ is a relation and $e_1$, $e_2$ are entities. Distant supervision is one such technique which tries to automatically generate training examples based on an existing KB such as Freebase. This paper is a survey of some of the techniques in distant supervision which primarily rely on Probabilistic Graphical Models (PGMs).