Paper: Distant Supervision for Relation Extraction with Matrix Completion

ACL ID P14-1079
Title Distant Supervision for Relation Extraction with Matrix Completion
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

The essence of distantly supervised rela- tion extraction is that it is an incomplete multi-label classification problem with s- parse and noisy features. To tackle the s- parsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification as completing the unknown labels of testing items (entity pairs) in a s- parse matrix that concatenates training and testing textual features with training label- s. Our algorithmic framework is based on the assumption that the rank of item-by- feature and item-by-label joint matrix is low. We apply two optimization model- s to recover the underlying low-rank ma- trix leveraging the sparsity of feature-label matrix. The matrix completion problem i...