Improving relation extraction beyond sentence boundaries using attention

Abstract

Reation Extraction(RE) is the subprocess of Information Extraction(IE) which focuses on determining and extracting the reation between two participating entities. Most of the past work focus on extracting relations within a sentence. Nowadays, research on relation extraction focuses on identifying and determining relationship between participating entities across sentences. This paper proposes a bi-directional GRU model with self attention mechanism for inter-sentential relation extraction. First, a bi-directional GRU with self attention mechanism is used to capture the information about the relation from intermediary terms between two entities. Then a bi-directional GRU is used to capture the information represented by entities, which plays a vital role in relation extraction. Finally, the proposed model combines both word embeddings and entity embeddings for extracting a relation. Experimental results show that the proposed Bi-directional GRU model can deliver state-of-the-art results on relation classification. Application of self attention mechanism on intermediary terms improves the performance of relation extraction. Experimental results show that F-measure of the proposed inter-sentential relation extraction is 0.75, which is better than state-of-the-art systems of inter-sentential relation extraction with the same dataset.