基于集成学习方法的实体关系抽取

基于神经网络的实体关系抽取模型已经被证明了它的有效性, 但使用单一的神经网络模型在不同的输入条件下, 会表现出不同的结果, 性能不太稳定. 因此本文提出一种利用集成学习思想将多个单一模型集成为一个综合模型的方法. 该方法主要使用MLP (MultiLayer Perceptron)将两个单一模型Bi-LSTM (Bi-directional Long Short-Term Memory)和CNN (Convolutional Neural Network)集成为一个综合模型, 该模型不仅可以充分利用两个单一模型的优势, 而且可以利用MLP的自学习能力与自动分配权重的优势. 本研究在SemEval 2010 Task 8数据集上取得了87.7％的F1值, 该结果优于其他主流的实体关系抽取模型.

Entity Relation Extraction Based on Ensemble Learning Method

The entity relation extraction model based on neural networks has been proven effective, but a single neural network model is unstable because it can yield various results with different inputs. Therefore, this study proposes a method to integrate multiple single models into a comprehensive one using the idea of ensemble learning. Specifically, this method integrates Bi-directional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) into a comprehensive model through MultiLayer Perceptron (MLP), which cannot only fully take advantage of the two single models, but also make use of the self-learning ability and automatic weight allocation of MLP. This study obtains F1 of 87.7% on the SemEval 2010 Task 8 dataset, which is better than other mainstream entity relation extraction models.