Enhanced Distant Supervision with State-Change Information for Relation Extraction

Abstract

In this work, we introduce a method for enhancing distant supervision with state-change information for relation extraction. We provide a training dataset created via this process, along with manually annotated development and test sets. We present an analysis of the curation process and data, and compare it to standard distant supervision. We demonstrate that the addition of state-change information reduces noise when used for static relation extraction, and can also be used to train a relation-extraction system that detects a change of state in relations.

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