# EANT: Distant Supervision for Relation Extraction with Entity Attributes via Negative Training
## 2 Related Work  
[PERSON] et al. [9] proposed distant supervision to address the lack of large-scale annotated data in relation extraction supervision methods. The method aligns the KGs with the corpus and labels sentences containing entity pairs from the KGs as the relation in the KGs. Although this approach generates annotated data quickly without manual effort, the generated data contains much noise, which degrades the performance of the RE model. To solve the noise problem, [PERSON] et al. [22] proposed the at-least-once assumption, where sentences with the same entity pairs were formed into a bag. It is the bag that expresses the relation rather than an individual sentence. Meanwhile, they designed lexical features, syntactic features, named entity labeling features, and predicted relations using machine learning methods, improving the model's results.  
Although these feature-based approaches are well designed, this is not enough. The NLP tools they used had inevitable errors, which led to the propagation or accumulation of errors. With the successful application of deep learning methods, scholars have started to extract features using deep neural networks.  
These approaches can be broadly classified into the following three categories: the first category of approaches makes full use of the non-noisy sentences in the dataset to extract rich information to generate more accurate representations for the sentences. [PERSON] et al. [10] used sentence-level selective attention to assign different weights to each sentence in the dataset to distinguish their impact. [PERSON] et al. [11] used entity description information as supplementary background knowledge to strengthen the learning of entity representation. [PERSON] et al. [23] argued that only a few words in a sentence were relevant to the expressed relation and assigning higher weights to these words can help to improve the RE performance.  
The second category of approaches attempts to filter out noisy sentences from the original dataset to eliminate their influence. [PERSON] et al. [13] and [PERSON] et al. [14] trained an instance selector using reinforcement learning with a policy function to decide whether to keep an instance or not. At the same time, the performance of the relation classifier was readjusted as a reward or penalty for reinforcement learning. [PERSON] et al. [15] chose generative adversarial networks to train generators and discriminators that achieved similar noise filtering effects. [PERSON] et al. [16] extracted the expression pattern of the relation from the sentences and removed the sentences that did not match pattern as noise.  
The third category of approaches is trained with noisy data and aims to generate relation labels for instances that better match the true expression of the sentence. [PERSON] et al. [18] proposed a soft-label noise reduction method that dynamically assigned a new label to a sentence during the training period based on the semantic information of the sentence. [PERSON] et al. [19] used unsupervised deep clustering to generate reliable labels for noisy sentences. [PERSON] et al. [20] described the noise patterns by dynamically generating a transition matrix, which indicated the possibility that the DS-labeled relation was confused for each instance.  
In this paper, we combine the features of the above three approaches and incorporate the attributes of entities as additional information into the model to obtain a more accurate sentence representation. Meanwhile, we use the NT method to train a noise filter to filter noisy sentences from the dataset. After filtering out the noisy sentences, we assign possible labels according to their confidence levels to utilize the information in them.