RDRS: Represent Document-level Relation with Sentence-level Relation by Distant Supervision

Abstract

This paper aims to study how to use remote monitoring for relation extraction, so as to identify the relationship between entities and provide logical facts. In recent years, document-level relation extraction (DocRE) has attracted much attention. Distant supervision (DS) has been playing an important role in sentence-level relation extraction (RE). Compared with traditional relation extraction methods, DS requires less data. However, the performance of document-level DS is limited by the noise in the long distant text. Previous DS works represent intra-sentential and inter-sentential representations in the same way, and neglect the difference between noise in them. This paper proposes an architecture called RDRS, which represents inter-sentence and intra-sentence relations in different ways, and uses a sentence-level relation model to represent document-level relations. The RDRS contains various modules, in which pairs of entities are separated according to the distribution of their mentions. In addition, this paper also improves the performance of inter-sentence representation by associating intra-sentence representation with inter-sentence representation. The experiments are carried out on Document-level Relation Extraction Dataset (DocRED). Experiments on DocRED show that the method has a significant improvement over the baseline. This model is better than previous models that represent inter-sentence and intra-sentence relations in the same way.

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