A Distantly-Supervised Relation Extraction Method Based on Selective Gate and Noise Correction (2023)

Abstract: “Entity relation extraction, as a core task of information extraction, aims to predict the relation ofentity pairs identified by text, and its research results are applied to various fields. To addressthe problem that current distantly supervised relation extraction (DSRE) methods based on large-scale corpus annotation generate a large amount of noisy data, a DSRE method that incorporatesselective gate and noise correction framework is proposed. The selective gate is used to reason-ably select the sentence features in the sentence bag, while the noise correction is used to correctthe labels of small classes of samples that are misclassified into large classes during the modeltraining process, to reduce the negative impact of noisy data on relation extraction. The resultson the English datasets clearly demonstrate that our proposed method outperforms other base-line models. Moreover, the experimental results on the Chinese dataset indicate that our methodsurpasses other models, providing further evidence that our proposed method is both robust andeffective.”

## introduction entity relation extraction (re) is a crucial task in information extraction that aims to identify the relation between entity pairs in text. the findings of re have practical applications in several fields, such as the construction of knowledge graphs (kg), semantic web annotation, and the development and optimization of question-and-answer systems and search engines, which have a significant impact on daily life. however, the task of re is challenging due to the limited availability of annotated data. to address this challenge, distant supervision has been proposed, which automatically annotates data, significantly increasing the number of annotated samples. however, distant supervision suffers from a strong hypothesis, leading to a large number of noisy labels during data annotation. training on a dataset with noisy labels can result in model overfitting to the noise, which adversely impacts the model's performance @xcite . to mitigate these issues, this paper proposes a novel method for re that incorporates selective gate and the end-to-end noise correction method. in our model, selective gate is utilized to rationally select sentence features in the sentence bag, while noise correction is used to correct the labels of small classes of samples that are misclassified into larger classes during model training. these techniques reduce the negative impact of noisy data on the distant dsre model. additionally, since common word embedding models, such as word2vec and glove, produce static vectors that overlook contextual semantics and the flexible use of multiple-meaning words, this paper introduces a pre-trained language model (plm) to encode and extract features from sentences. this approach provides richer sentence semantic features, effectively improving prediction accuracy and reducing training time. experiment results demonstrate that this method significantly outperforms the baseline models, improving the dsre model's performance. the major contributions of this paper can be summarized as follows: empirical experiments demonstrate the robustness and effectiveness of our proposed method. ## methodology to mitigate the impact of noise on the dsre model, this paper proposes a two-pronged approach, plmbased selective gate pencil (plmg-pencil) method. as shown in figure 1 , first, we encode the text using plm and employ the selective gate mechanism to select sentence-level features that contribute to the bag-level feature. second, we replace all labels with soft labels and train the model in the pencil framework. this framework uses soft labels that are updated during training and can be corrected for noisy data. this approach reduces the chances of noise being selected in the selective gate, even if it cannot be corrected in the pencil framework. these two methods complement each other, reducing the degree of noise interference and improving the model's re performance. in this section, we will describe our approach from the backbone model architecture, noise correction framework, and the re algorithm. ## conclusion in this paper, we propose the plmg-pencil method for dsre. our approach automatically learns the weights of different sentences in a sentence bag and selects the features that best represent the sentence bag through a gate mechanism. additionally, we introduce a noise correction framework based on end-c c l 2 0 2 3 to-end probability with noise label learning for improved performance in re. the experimental results clearly demonstrate that our proposed model outperforms baselines and achieves significant improvement in the re task. our approach shows great potential for practical application in the field of information extraction.