Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration (2022)

Abstract: Open relation extraction is the task to extract relational facts without pre-defined relation types from open-domain corpora. However, since there are some hard or semi-hard instances sharing similar context and entity information but belonging to different underlying relation, current OpenRE methods always cluster them into the same relation type. In this paper, we propose a novel method based on Instance Ranking and Label Calibration strategies (IRLC) to learn discriminative representations for open relation extraction. Due to lacking the original instance label, we provide three surrogate strategies to generate the positive, hard negative, and semi-hard negative instances for the original instance. Instance ranking aims to refine the relational feature space by pushing the hard and semi-hard negative instances apart from the original instance with different margins and pulling the original instance and its positive instance together. To refine the cluster probability distributions of these instances, we introduce a label calibration strategy to model the constraint relationship between instances. Experimental results on two public datasets demonstrate that our proposed method can significantly outperform the previous state-of-the-art methods.

## introduction open relation extraction (openre) has been proposed to extract new relational facts where the types of target relations are not pre-defined. previous methods can be classified into two types: open information extraction (openie) and unsupervised relation discovery. for openie @xcite @xcite , the relations are directly represented by relation phrases * the first three authors contribute equally. yajing xu is the corresponding author. as shown in figure 1 (a), there are two types of negative instances for the relation type born_in_place: hard negative and semihard negative. for semi-hard negative instances like s 4 , openre models will assign s 1 and s 4 into the same relation type born_in_place since s 1 and s 4 share similar context information. this problem can be even more severe if the representation space exists some hard negative instances like s 3 , because s 3 possesses a similar context "was born in "and similar entity "jon" to s 1 . an intuitive way to solve this problem is to refine the relational feature space, as shown in figure 1 @xcite . besides, all instances should follow the same relative relationship in the label semantic space which means the original and positive instances have a more similar cluster probability distribution than the hard and semi-hard negative instances. therefore, it is important to model the constraint relationship between these instances in the label semantic space. in this paper, we propose a novel method based on instance ranking and label calibration strategies (irlc) to better identify the hard and semihard negative instances by learning discriminative representations in relational feature and label semantic space simultaneously. however, due to lacking of the instance label, we cannot directly obtain the positive, hard negative and semi-hard negative instances of the original instance. to solve this, we use three data augmentation strategies to generate the positive, hard negative and semi-hard negative instances for the original instance. to refine the relational feature space, we introduce instance ranking to make the original instance close to its positive instance and away from its hard and semi-hard negative instances. to correct the cluster assignment probabilities of hard and semi-hard negative instances, and keep the probability distributions of the original and positive instances aligned, in the label semantic space, label calibration strategy is designed to model two constraint relationships between the original and hard negative instance, and between the hard and semi-hard negative instance. to summarize, the major contributions of our work are as follows: (1) we propose a novel method based on instance ranking and label calibration to learn discriminative representations in relational feature and label semantic space simultaneously. (2) we introduce three surrogate strategies to generate the positive, hard negative and semi-hard negative instances under unsupervised manner. (3) experimental results show that our proposed method significantly outperforms the previous state-of-the-art models with the improvements of average performance of 11.1% and 11.8%, on two datasets respectively. tokyo, japan, contains many parks and gardens. original positive hard negative semi-hard negative in tokyo, japan, there are many parks and gardens. kyoto, japan contains many parks and gardens. guangzhou, china contains many parks and gardens. ## method in this work, we propose a novel method to learn relation representations in feature and semantic space simultaneously. as shown in figure 2 , our method mainly consists of three components: data augmentation, instance ranking, and label calibration modules. we will introduce these module details in the following subsections. ## conclusion in this paper, we propose a novel method based on instance ranking and label calibration (irlc) to learn discriminative representations for better identifying the hard and semi-hard negative intances, in the relational feature and label semantic space simultaneously. due to lacking the label of each instance, we introduce three surrogate strategies to generate the augmented views for the original instance. and then instance ranking is used to refine the relational feature space, and label calibration is designed to model the constraint relationship between instances. experiments and analysis confirm the effectiveness of irlc for openre.