  We explore methods to extract relations between named entities from free text
in an unsupervised setting. In addition to standard feature extraction, we
develop a novel method to re-weight word embeddings. We alleviate the problem
of features sparsity using an individual feature reduction. Our approach
exhibits a significant improvement by 5.8% over the state-of-the-art relation
clustering scoring a F1-score of 0.416 on the NYT-FB dataset.
