Improving Performance of Relation Extraction Algorithm via Leveled Adversarial PCNN and Database Expansion

Abstract

This study introduces database expansion using the Minimum Description Length (MDL) algorithm to expand the database for better relation extraction. Different from other previous relation extraction researches, our method improves system performance by expanding data. The goal of database expansion, together with a robust deep learning classifier, is to diminish wrong labels due to the incomplete or not found nature of relation instances in the relation database (e.g., Freebase). The study uses a deep learning method (Piecewise Convolutional Neural Network or PCNN) as the base classifier of our proposed approach: the leveled adversarial attention neural networks (LATTADV-ATT). In the database expansion process, the semantic entity identification is used to enlarge new instances using the most similar itemsets of the most common patterns of the data to get its pairs of entities. About the deep learning method, the use of attention of selective sentences in PCNN can reduce noisy sentences. Also, the use of adversarial perturbation training is useful to improve the robustness of system performance. The performance even further is improved using a combination of leveled strategy and database expansion. There are two issues: 1) database expansion method: rule generation by allowing step sizes on selected strong semantic of most similar itemsets with aims to find entity pair for generating instances, 2) a better classifier model for relation extraction. Experimental result has shown that the use of the database expansion is beneficial. The MDL database expansion helps improvements in all methods compared to the unexpanded method. The LATTADV-ATT performs as a good classifier with high precision P@100=0.842 (at no expansion). It is even better while implemented on the expansion data with P@100=0.891 (at expansion factor k=7).

Interaction Information Guided Prototype Representation Rectification for Few-Shot Relation Extraction

“…Although regularization techniques are widely used to mitigate overfitting in deep learning models, they do not provide additional supervised information for the model. Therefore, when the amount of labelled data is insufficient, simply applying regularization may not effectively solve the generalization problem [27]. To address the issue of limited training data and reduce the manual annotation cost, Mintz et al [28] first proposed using distant supervision for automatic data annotation.…”

Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

“…This strategy extracts relations of entity pairs from sentence bags, with the purpose of alleviating the sentence-level label noise. In addition, some novel strategies, such as reinforcement learning [11], [12], adversarial training [13], [14], [15], [16] and deep clustering [17], [18] also show great potential for DSRE. However, these approaches are driven totally by the noisy labeling data, which may misguide the optimization of parameters and further hurt the reliability of models.…”