Remotely-supervised Dual-Attention relation classification method and system

Abstract

The invention relates to a remotely-supervised Dual-Attention relation classification method and system. The method comprises the following steps: aligning entity pair in a knowledge base to news linguistic data through remote supervision, and constructing an entity pair sentence set; performing word-level vector encoding on the sentence through a Bi-LSTM model based on a word-level attention mechanism so as to obtain a semantic feature encoding vector of the sentence; performing encoding and denoising on the semantic feature of the sentence through the Bi-LSTM model based on the sentence-level attention mechanism so as to obtain a sentence set feature encoding vector; and packing the sentence set feature encoding vector and the entity pair translation vector, and performing the relation classification of the entity pair on the obtained packet feature. Through the technical scheme provided by the invention, the noise data of the model training is reduced, the artificial data annotationand the caused error transmission thereof are avoided. The entity alignment is performed by applying the open domain text and the large-scale knowledge library, and the annotation data scale problemof the relation extraction is effectively solved.

Description

technical field [0001] The invention belongs to the field of relationship classification, and in particular relates to a remote-supervised Dual-Attention relationship classification method and system. Background technique [0002] With the development of Internet technology, a large amount of text information on the World Wide Web has grown rapidly, and the technology of automatically extracting knowledge from text information has attracted more and more attention, and has become a current hot spot. The current mainstream relationship extraction method is a relationship classification method based on neural network learning, which mainly faces three major problems: difficulties in the representation and mining of semantic features, error transmission caused by manual annotation, and noise impact of model training. At present, among the relationship classification methods based on neural network learning, the relationship classification methods that achieve the best results a...

AI Technical Summary

Problems solved by technology

The current mainstream relationship extraction method is a relationship classification method based on neural network learning, which mainly faces three major problems: difficulties in the representation and mining of semantic features, error transmission caused by manual labeling, and noise impact of model training

Although some improved convolutional network models can achieve modeling of larger span information by superimposing structures such as K-segment maximum pooling, such as the experiment of three-segment pooling through PCNNs (Piecewise CNNs), the maximum pooling method is relatively For Bi-LSTM, when extracting semantic features with long dependencies such as long texts, the cost is relatively high and the performance is relatively weak

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