Extracting relations out of unstructured text is essential for a wide range of applications. Minimal human effort, scalability and high precision are desirable characteristics. We introduce a distant supervised closed relation extraction approach based on distributional semantics and a tree generalization. Our approach uses training data obtained from a reference knowledge base to derive dependency parse trees that might express a relation. It then uses a novel generalization algorithm to construct dependency tree patterns for the relation. Distributional semantics are used to eliminate false candidate patterns. We evaluate the performance in experiments on a large corpus using ninety target relations. Our evaluation results suggest that our approach achieves a higher precision than two state-of-the-art systems. Moreover, our results also underpin the scalability of our approach. Our open source implementation can be found at https://github.com/dice-group/Ocelot.
@InProceedings{
author="Speck, Ren{\'e} and Ngomo Ngonga, Axel-Cyrille",
title="On Extracting Relations Using Distributional Semantics and a Tree Generalization",
booktitle="Knowledge Engineering and Knowledge Management",
year="2018",
publisher="Springer International Publishing",
isbn="978-3-030-03667-6"
}Java 8, Maven 3
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