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arXiv · 1801.07174

Unsupervised Open Relation Extraction

Hady Elsahar·Elena Demidova·Simon Gottschalk·Christophe Gravier·Frederique Laforest
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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.

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Hady Elsahar, Elena Demidova, Simon Gottschalk, Christophe Gravier, Frederique Laforest. 2018-01-22. Unsupervised Open Relation Extraction. https://doi.org/10.1007/978-3-319-70407-4_3

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