Semi-Supervised Bootstrapping of Relationship Extractors with Distributional Semantics

David S. Batista, Bruno Martins, Mário J. Silva

Abstract: Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed relationships while limiting the semantic drift. We research bootstrapping for relationship extraction using word embeddings to find similar relationships. Experimental results show that relying on word embeddings achieves a better performance on the task of extracting four types of relationships from a collection of newswire documents when compared with a baseline using TFIDF to find similar relationships.