@demidova

Unsupervised Open Relation Extraction

, , , , and . Proceedings of the ESWC 2017 Satellite Events, Lecture Notes in Computer Science (LNCS), vol 10577., Springer, (2017)
DOI: 10.1007/978-3-319-70407-4_3

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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