Segmented pooling relationship extraction method based on convolutional neural network

Abstract

The invention discloses a segmented pooling relationship extraction method based on a convolutional neural network. The method comprises the following steps of 1, performing vector mapping on a text based on a pre-training word vector, a random word vector and a zero vector; 2, performing convolution operation on the vector matrix through a neural network to extract features; 3, performing segmentation pooling on the convolution result to further abstract features; and 4, performing full connection, and predicting a result on a Softmax layer. The sentence text complete information is fully utilized; an entity segmentation strategy is adopted, a neural network technology is introduced, the characteristic that a neural network automatically extracts high-dimensional abstract features in a layered mode is brought into full play, pooling features of all parts of a text segmented by entities are extracted, the feature sparsity problem generated by a traditional machine learning method is avoided to a certain extent, and therefore the relation extraction performance is improved.

Description

technical field [0001] The invention relates to a convolutional neural network, in particular to a convolutional neural network-based segmentation pooling relation extraction method, which belongs to the technical field of natural language processing. Background technique [0002] With the rapid popularization of computers around the world and the rapid development of Internet technology, all kinds of data such as video, audio, pictures, and text have surged, and a large amount of information appears in front of users in electronic digital form. In order to cope with the severe challenges brought by the information explosion, there is an urgent need for professional automated tools to extract truly valuable information from massive amounts of data, and information extraction has emerged as the times require. Information extraction technology is a widely used information processing technology in the field of natural language processing, and relation extraction is an important...

AI Technical Summary

Problems solved by technology

In 2013, Shao Kun et al. used pattern matching to extract structured information and used a dynamic pattern library to improve the accuracy of extraction. However, the structure of word segmentation and the existence of professional vocabulary will affect the recognition effect.

This method can effectively consider the syntactic structure information of the sentence, but cannot consider the position and semantic information of the two entities in the sentence

Semi-supervised methods such as bootstrap methods reduce the dependence on labeled corpus in the training process and reduce the cost of manual labeling, but there is a problem of semantic drift

Unsupervised methods mainly use clustering algorithms, which can be applied to large-scale open information fields, but it is difficult to accurately describe the relationship names

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