Discovering relations using matrix factorization methods

TL;DR: This work proposes the use of matrix factorization methods instead of clustering and studies Non-Negative Matrix Factorization (NMF) and Boolean Matrix Factorized (BMF), which perform better than the k-means clustering in this evaluation.

Abstract: Traditional relation extraction methods work on manually defined relations and typically expect manually labelled extraction patterns for each relation. This strongly limits the scalability of these systems. In Open Relation Extraction (ORE), the relations are identified automatically based on co-occurrences of "surface relations" (contexts) and entity pairs. The recently-proposed methods for ORE use partition clustering to find the relations. In this work we propose the use of matrix factorization methods instead of clustering. Specifically, we study Non-Negative Matrix Factorization (NMF) and Boolean Matrix Factorization (BMF). These methods overcome many problems inherent in clustering and perform better than the k-means clustering in our evaluation.