Using Entity Information from a Knowledge Base to Improve Relation Extraction
Lan Du, Anish Kumar, Mark S. Johnson, Massimiliano Ciaramita · Monash University Research Portal (Monash University) · 2015
Relation extraction is the task of ex-tracting predicate-argument relationships between entities from natural language text. This paper investigates whether back-ground information about entities avail-able in knowledge bases such as FreeBase can be used to improve the accuracy of a state-of-the-art relation extraction sys-tem. We describe a simple and effective way of incorporating FreeBase’s notable types into a state-of-the-art relation extrac-tion system (Riedel et al., 2013). Experi-mental results show that our notable type-based system achieves an average 7.5% weighted MAP score improvement. To understand where the notable type infor-mation contributes the most, we perform a series of ablation experiments. Results show that the notable type information im-proves relation extraction more than NER labels alone across a wide range of entity types and relations. 1