Improving Distant Supervised Relation Extraction by Dynamic Neural Network

Distant Supervised Relation Extraction (DSRE) is usually formulated as a problem of classifying a bag of sentences that contain two query entities, into the predefined relation classes. Most existing methods consider those relation classes as distinct semantic categories while ignoring their potential connection to query entities. In this paper, we propose to leverage this connection to improve the relation extraction accuracy. Our key ideas are twofold:(1) For sentences belonging to the same relation class, the expression style, ie words choice, can vary according to the query entities. To account for this style shift, the model should adjust its parameters in accordance with entity types.(2) Some relation classes are semantically similar, and the entity types appear in one relation may also appear in others. Therefore, it can be trained cross different relation classes and further enhance those classes with few samples, ie …