Unsupervised Open Relation Extraction
Table 1: Comparison between different features for clustering.
['Feature', 'F1']
This dataset contains approximately 1.8M sentences divided into 80%-20% test-validation splits and aligned automatically to the statements (triples) from Freebase. The alignment between sentences and the properties of the Freebase triples in this dataset is considered as the gold standard for the relation clustering algorithm. We use the validation split to tune the parameters for re-weighting word vectors and the PCA algorithm, and the test set for evaluating relation discovery methods. To make our results comparable we set the number of relations to induce (number of clusters k) to 100, following the SOA systems. Results demonstrate that our method of word embeddings re-weighted by the dependency path shows a significant improvement over other traditional sentence representations. Our method outperforms the state-of-the-art relation discovery algorithm scoring a pairwise F1 score of 41.6%.