Effective Attention Modeling for Neural Relation Extraction
Tapas K. Nayak, Hwee Tou Ng · 2019
Relation extraction is the task of determining the relation between two entities in a sentence.Distantly-supervised models are popular for this task.However, sentences can be long and two entities can be located far from each other in a sentence.The pieces of evidence supporting the presence of a relation between two entities may not be very direct, since the entities may be connected via some indirect links such as a third entity or via coreference.Relation extraction in such scenarios becomes more challenging as we need to capture the long-distance interactions among the entities and other words in the sentence.Also, the words in a sentence do not contribute equally in identifying the relation between the two entities.To address this issue, we propose a novel and effective attention model which incorporates syntactic information of the sentence and a multi-factor attention mechanism.Experiments on the New York Times corpus show that our proposed model outperforms prior state-of-the-art models.