Held-out versus Gold Standard: Comparison of Evaluation Strategies for Distantly Supervised Relation Extraction from Medline abstracts

Abstract

Distant supervision is a useful technique for creating relation classifiers in the absence of labelled data. The approaches are often evaluated using a held-out portion of the distantly labelled data, thereby avoiding the need for lablelled data entirely. However, held-out evaluation means that systems are tested against noisy data, making it difficult to determine their true accuracy. This paper examines the effectiveness of using held-out data to evaluate relation extraction systems by comparing the results that are produced with those generated using manually labelled versions of the same data. We train classifiers to detect two UMLS Metathesaurus relations (may-treat and may-prevent) in Medline abstracts. A new evaluation data set for these relations is made available. We show that evaluation against a distantly labelled gold standard tends to overestimate performance and that no direct connection can be found between improved performance against distantly and manually labelled gold standards.