Paper: Ensemble Semantics for Large-scale Unsupervised Relation Extraction

ACL ID D12-1094
Title Ensemble Semantics for Large-scale Unsupervised Relation Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

Discovering significant types of relations from the web is challenging because of its open nature. Unsupervised algorithms are developed to extract relations from a cor- pus without knowing the relations in ad- vance, but most of them rely on tagging arguments of predefined types. Recently, a new algorithm was proposed to jointly extract relations and their argument se- mantic classes, taking a set of relation in- stances extracted by an open IE algorithm as input. However, it cannot handle poly- semy of relation phrases and fails to group many similar (?synonymous?) rela- tion instances because of the sparseness of features. In this paper, we present a novel unsupervised algorithm that provides a more general treatment of the polysemy and synonymy problems. The algorithm inco...