Unsupervised Open Relation Extraction

01/22/2018

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by   Hady Elsahar, et al.

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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 clustering scoring a F1-score of 0.416 on the NYT-FB dataset.