Unsupervised Open Relation Extraction
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.
KeywordsRelation extraction Word embedding NLP
This work was partially funded by H2020-MSCA-ITN-2014 WDAqua (64279), ALEXANDRIA (ERC 339233) and Data4UrbanMobility (BMBF).
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