Semantic Enhanced Distantly Supervised Relation Extraction via Graph Attention Network

Abstract

Distantly Supervised relation extraction methods can automatically extract the relation between entity pairs, which are essential for the construction of a knowledge graph. However, the automatically constructed datasets comprise amounts of low-quality sentences and noisy words, and the current Distantly Supervised methods ignore these noisy data, resulting in unacceptable accuracy. To mitigate this problem, we present a novel Distantly Supervised approach SEGRE (Semantic Enhanced Graph attention networks Relation Extraction) for improved relation extraction. Our model first uses word position and entity type information to provide abundant local features and background knowledge. Then it builds the dependency trees to remove noisy words that are irrelevant to relations and employs Graph Attention Networks (GATs) to encode syntactic information, which also captures the important semantic features of relational words in each instance. Furthermore, to make our model more robust against noisy words, the intra-bag attention module is used to weight the bag representation and mitigate noise in the bag. Through extensive experiments on Riedel New York Times (NYT) and Google IISc Distantly Supervised (GIDS) datasets, we demonstrate SEGRE’s effectiveness.

Distant Supervision for Relation Extraction with Sentence Selection and Interaction Representation

“…Shi et al [30] propose an advanced graph neural network, which assigns higher weights to those direct neighbor words that contribute more to relation prediction through breadth exploration. Ouyang et al [31] use graph attention networks to encode syntactic features, which obtain the important semantic information of related words in each sentence. Phi et al [32] combine a bidirectional gated recurrent unit (BiGRU) model with a form of hierarchical attention that enhances the performance of the distantly supervised RE task.…”

Distant supervision relation extraction based on mutual information and multi-level attention

“…Secondly, word-level methods usually assign weight to the words in the instance according to some criteria such as distance, without considering the role of words in the semantic expression of sentences [31,12,19,30]. As mentioned above, mutual information reflects the commonality between sentence, so mutual information can Tab.…”