A Multi-Channel Deep Neural Network for Relation Extraction

The task of relation recognition identifies semantic relationships between two named entities in a sentence. In neural network based models, a convolutional layer is often conducted to extract representative local features of a sentence. The convolution operation is implemented through a whole sentence, without considering the structure of a sentence. Because the task to recognize entity relation is processed in sentence level, many ambiguous phenomena (e.g., polysemy) are influential rather than in a document. Capturing structural information of a sentence is helpful to solve this problem. In this paper, a multi-channel framework is presented, which uses two named entities to divide a sentence into several channels. Each channel is stacked with layered neural networks. These channels do not interact during recurrent propagation, which enables a neural network to learn different representations. In our experiments, it outperforms the widely used position embedding approach. Comparing with the state-of-the-art approaches, its performance shows a meaningful improvement.

Abstract licence: CC BY-NC-ND

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