# EANT: Distant Supervision for Relation Extraction with Entity Attributes via Negative Training
## 1 Introduction  
Knowledge graphs (KGs) describe real-world concepts and their relations in graphs and play an essential role in natural language processing tasks, such as recommendation systems [1, 2], search engines [3], and Q&A systems [4, 5]. Currently, open-source available KGs such as FreeBase [6], DBPedia [7], and Wikidata [8] have been widely used. Although they already contain hundreds of millions of relation facts, they still cannot cover the nearly infinite number of facts in the real world. To improve the completeness of KGs, relation extraction (RE) is proposed to automatically extract the relation between two given entities from unstructured text. Most existing supervised RE methods require large amounts of annotated data, which is often expensive and time-consuming. To address this problem, [PERSON] et al. [9] proposed a distant supervision (DS) approach to generate training data automatically. It heuristically aligns the corpus with the KGs and assumes that if two entities have a relation in the KGs, then sentences that contain both entities may express the relation. However, it is clear that this assumption is too strong and inevitably introduces noise problems, such as false positives (FP) and false negatives (FN). The former is because the sentence does not actually express the target relation but is incorrectly marked as positive; the latter is due to the sentence actually expressing the target relation but being marked as negative because of the missing corresponding facts in the KGs. These two types of noise seriously affect the RE performance of the model. The current noise reduction methods can be broadly classified into four categories: (1) feature-based methods that manually construct suitable features and filter noise using machine learning; (2) representation-enhancing methods that improve the contribution of valid sentences in the dataset using attention mechanisms [10], extra information [11], pre-trained models [12], etc. ; (3) noise-filtering methods that filter out noisy sentences usingreinforcement learning [13; 14], adversarial learning [15], pattern matching [16; 17], etc. ; (4) relabeling methods that assign correct relation labels to sentences using soft labeling [18], re-labeling [19], noise modeling [20], etc. However, none of these approaches take full advantage of the rich information contained in entity attributes (e.g., description, alias, and types). Such information can provide richer background knowledge of entities for sentence representation, and incorporating them can improve the performance of the model. As shown in Figure 1, given the sentence \"[PERSON] and [PERSON] founded Google in 1998 on the back of PageRank algorithm.\", the task of RE is to discover the relation \"_Founder_of\"_ between the entity \"_[PERSON]_\" and \"_Google_\". From this example, we can clearly see the role of entity attribute information: the description of \"_[PERSON]_\" mentions that \"[PERSON] is the founding CEO of Google\", which undoubtedly provides valid evidence for the relation \"_Founder_of\"_. Additionally, the alias of \"_Google_\" as \"Google Inc\" can provide more information of \"Google is a company\" to the entity \"_Google_\". By extracting these features, we can effectively determine similar relations. Finally, types information can reduce the probability that sentences are classified as impossible relations. For example, if \"_[PERSON]_\" is an instance of \"person\" and the tail entity \"_Google_\" is an instance of \"company\", then it definitely not be an instance of the relation \"_Place_of_birth_\". Moreover, since the DSRE dataset is automatically generated from aligned KGs, previous methods often lack explicit supervised information to capture the noise. Therefore, we propose to introduce negative training (NT) [21] to train the noise selector to achieve the separation of noisy data.