Abstract

Distantly-supervised Relation Extraction (RE) methods train an extractor by automatically aligning relation instances in a Knowledge Base (KB) with unstructured text. In addition to relation instances, KBs often contain other relevant side information, such as aliases of relations (e.g., founded and co-founded are aliases for the relation founderOfCompany). RE models usually ignore such readily available side information. In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction. It uses entity type and relation alias information for imposing soft constraints while predicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode syntactic information from text and improves performance even when limited side information is available. Through extensive experiments on benchmark datasets, we demonstrate RESIDE’s effectiveness. We have made RESIDE’s source code available to encourage reproducible research.

Highlights

  • The construction of large-scale Knowledge Bases (KBs) like Freebase (Bollacker et al, 2008) and Wikidata (Vrandecicand Krotzsch, 2014) has proven to be useful in many natural language processing (NLP) tasks like question-answering, web search, etc

  • Through extensive experiments on benchmark datasets, we demonstrate RESIDE’s effectiveness over state-of-the-art baselines

  • We find that RESIDE achieves higher precision over the entire recall range on both the datasets

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Summary

Introduction

The construction of large-scale Knowledge Bases (KBs) like Freebase (Bollacker et al, 2008) and Wikidata (Vrandecicand Krotzsch, 2014) has proven to be useful in many natural language processing (NLP) tasks like question-answering, web search, etc. On employing GCN, we get an updated d-dimensional hidden representation hv ∈ Rd, ∀v ∈ V, by considering only its immediate neighbors (Kipf and Welling, 2017). This can be formulated as: hv = f (Wluv xu + bluv ) .

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