Unsupervised Open Relation Extraction
arXiv:1801.07174 · doi:10.1007/978-3-319-70407-4_3
Abstract
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.
4 pages, published in ESWC 2017