Paper: Noisy Or-based model for Relation Extraction using Distant Supervision

ACL ID D14-1208
Title Noisy Or-based model for Relation Extraction using Distant Supervision
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Distant supervision, a paradigm of rela- tion extraction where training data is cre- ated by aligning facts in a database with a large unannotated corpus, is an attractive approach for training relation extractors. Various models are proposed in recent lit- erature to align the facts in the database to their mentions in the corpus. In this paper, we discuss and critically analyse a popular alignment strategy called the ?at least one? heuristic. We provide a sim- ple, yet effective relaxation to this strat- egy. We formulate the inference proce- dures in training as integer linear program- ming (ILP) problems and implement the relaxation to the ?at least one ? heuris- tic via a soft constraint in this formulation. Empirically, we demonstrate that this sim- ple strategy leads to a better per...