A Survey of Distant Supervision Methods using PGMs
It provides a review of existing methods for researchers in relation extraction, but is incremental as it does not introduce new techniques.
This paper surveys techniques in distant supervision for relation extraction, focusing on methods that use probabilistic graphical models to automatically generate training examples from existing knowledge bases like Freebase.
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).