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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Accessing the clustering output by HAC at rank k giving k clusters.
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Acknowledgements
This work was partially funded by H2020-MSCA-ITN-2014 WDAqua (64279), ALEXANDRIA (ERC 339233) and Data4UrbanMobility (BMBF).
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Elsahar, H., Demidova, E., Gottschalk, S., Gravier, C., Laforest, F. (2017). Unsupervised Open Relation Extraction. In: Blomqvist, E., Hose, K., Paulheim, H., Ławrynowicz, A., Ciravegna, F., Hartig, O. (eds) The Semantic Web: ESWC 2017 Satellite Events. ESWC 2017. Lecture Notes in Computer Science(), vol 10577. Springer, Cham. https://doi.org/10.1007/978-3-319-70407-4_3
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DOI: https://doi.org/10.1007/978-3-319-70407-4_3
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