BiTCNN: A Bi-Channel Tree Convolution Based Neural Network Model for Relation Classification

Ren, Feng; Li, Yongcheng; Zhao, Rongsheng; Zhou, Di; Liu, Zhihui

doi:10.1007/978-3-319-99495-6_14

Cited by 1 publication

(1 citation statement)

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“…Recently, neural network-based methods [10,11] have been designed to address the problem, mainly including CNN [2,[12][13][14][15][16] and recurrent neural network (RNN)-based [17][18][19][20][21][22] methods. These methods [12,23,24] leverage the embedding to represent each token in the sentence, which capture the latent semantic features more effectively and overcome the semantic representation problem in feature-based and kernel-based methods.…”

Section: Related Workmentioning

confidence: 99%

Neural Relation Classification Using Selective Attention and Symmetrical Directional Instances

Tan

Huang

et al. 2018

Symmetry

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Relation classification (RC) is an important task in information extraction from unstructured text. Recently, several neural methods based on various network architectures have been adopted for the task of RC. Among them, convolution neural network (CNN)-based models stand out due to their simple structure, low model complexity and "good" performance. Nevertheless, there are still at least two limitations associated with existing CNN-based RC models. First, when handling samples with long distances between entities, they fail to extract effective features, even obtaining disturbing ones from the clauses, which results in decreased accuracy. Second, existing RC models tend to produce inconsistent results when fed with forward and backward instances of an identical sample. Therefore, we present a novel CNN-based sentence encoder with selective attention by leveraging the shortest dependency paths, and devise a classification framework using symmetrical directional-forward and backward-instances via information fusion. Comprehensive experiments verify the superior performance of the proposed RC model over mainstream competitors without additional artificial features.

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