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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Elsahar, H., Demidova, E., Gottschalk, S., Gravier, C., & Laforest, F. (2017). Unsupervised Open Relation Extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10577 LNCS, pp. 12–16). Springer Verlag. https://doi.org/10.1007/978-3-319-70407-4_3
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