###### Abstract  
Distant supervised relation extraction (DSRE) is widely used to extract novel relational facts from plain text, so as to improve the knowledge graph. However, distant supervision inevitably suffers from the noisy labeling problem that will severely damage the performance of relation extraction. Currently, most DSRE methods are mainly focused on reducing the weights of noisy sentences, ignoring the bag-level noise where all sentences in a bag are wrongly labeled. In this paper, we present a novel noise detection-based relation extraction approach (NDRE) to automatically detect noisy labels with entity information and dynamically correct them, which can alleviate both instance-level and bag-level noisy problems. By this means, we can extend the dataset from the Web tables without introducing more noise. In this approach, to embed the semantics of sentences from corpus and web tables, we firstly propose a powerful sentence coder that employs an internal multi-head self-attention mechanism between the piecewise max-pooling convolutional neural network. Second, we adopt a noise detection strategy, which is expected to dynamically detect and correct the original noisy label according to the similarity between sentence representation and entity-aware embeddings. Then, we aggregate the information from corpus and web tables to make the final relation prediction. Experimental results on a public benchmark dataset demonstrate that our proposed approach achieves significant improvements over the state-of-the-art baselines and can effectively reduce the noisy labeling problem. relation extraction; distant supervision; noise detection +
Footnote †: journal: Medical  
<e1, e2> will express the relation \(r\). Otherwise, if there is no relation between <e1, e2> in the knowledge graph, the sentence mentioning them will be labeled as \"not a relation\" (NA). DS has been widely applied in relation extraction and has achieved good results. At present, many studies have applied deep learning in DS and improved distant supervision relation extraction (DSRE) by automatically learning text features [4, 5, 6]. In addition, some scholars have found that the addition of additional knowledge can further improve DSRE, thus gaining great attention [7, 8, 9, 10]. Among them, [PERSON] et al. [10] proposed a hierarchical framework to fuse information from DS and web tables that share entity relationship facts, which greatly improves the efficiency of DSRE. However, the above methods follow the strong assumption of DS, and as this strong assumption does not always hold true, it might result in a wrong labeling problem. At the same time, adding additional knowledge can improve relationship extraction, but using two-hop DS data will even introduce more new noise because the strong assumption is not changed. On the issue of DS noise mitigation, a multi-instance learning (MIL) framework [11, 12, 13] is introduced into DSRE to relax the strong assumption to the at-least-one principle. In the MIL framework, all instances mentioning <e1, e2> constitute an instance-bag which shares a common label \(r\). The at-least-one principle states that at least one instance in the bag can imply the relation \(r\), and the bag is treated as the samples for training RE models instead of instances. However, above feature-based methods rely heavily on the accurate features derived by NLP tools, which might suffer from an error propagation problem. To alleviate the noise problem, many RE studies based on the MIL framework employ neural networks with a selective attention mechanism to assign weights to different instances within the bag [14, 15, 16, 17, 18], and all achieve good results. These selective attention methods still assign a certain weight to the noisy instances (false positive instances); especially when a bag composed of single instance is wrongly labeled, as illustrated in Table 1, selective attention will not work on such a bag-level noise problem.