Distantly Supervision for Relation Extraction via LayerNorm Gated Recurrent Neural Networks

Siheng Wei
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引用次数: 1

Abstract

Relation extraction is a classic task in the NLP field which aims to predict the relation between two entities in given sentences. Convolutional neural network (CNN) is one of the typical neural network structures applied to this task. However, the existing CNN model used for extraction is not able to capture the time information in sentences which leads a great contribution to predict the right directionality between the two entities. Therefore, I propose a new gated recurrent neural networks with layer normalization (LNGRU) to obtain the background information of the future and the past in sentences. Experiments demonstrate that my model is effective and superior to several comparable baseline models.
基于分层范数门控递归神经网络的关系抽取远程监控
关系抽取是自然语言处理领域的一项经典任务,其目的是预测给定句子中两个实体之间的关系。卷积神经网络(CNN)是应用于该任务的典型神经网络结构之一。然而,现有的用于提取的CNN模型无法捕获句子中的时间信息,这对预测两个实体之间的正确方向做出了很大贡献。因此,我提出了一种新的层归一化门控递归神经网络(LNGRU)来获取句子中未来和过去的背景信息。实验表明,该模型是有效的,优于几个可比较的基线模型。
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