T-REX: A Topic-Aware Relation Extraction Model

Abstract

Document-level relation extraction (RE) has recently received a lot of attention. However, existing models for document-level RE have similar structures to the models for sentence-level RE. Thus, they still do not consider some unique characteristics of the new problem setting. For example, in Wikipedia, there is a title for each page and it usually represents the topic entity that is mainly described on the page. In many cases, the topic entity is omitted in the text. Thus, existing RE models often fail to find the relations with the omitted topic entity. To tackle the problem, we propose a Topic-aware Relation EXtraction (T-REX) model. To extract the relations with the (possibly omitted) topic entity, the proposed model first encodes the topic entity by aggregating the information of all its mentions in the document. Then it finds the relations between the topic entity and each mention of other entities. Finally, the output layer combines the mention-wise results and outputs all relations expressed in the document. Our performance study with a large-scale dataset confirms the effectiveness of the T-REX model.