Unsupervised Open Relation Extraction

, , , , and . Proceedings of the ESWC 2017 Satellite Events, Lecture Notes in Computer Science (LNCS), vol 10577., Springer, (2017)


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.

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