A sentence backbone analysis method and system based on multi-task depth neural network of character segmentation and named entity recognition

Abstract

The invention provides a sentence backbone analysis method based on a multi-task depth neural network of character segmentation and named entity recognition, and a system. The invention uses three different bi-directional LSTM neural networks with conditional random fields to separate Chinese character segmentation corpus, Chinese named entity recognition corpus and Chinese sentence backbone analysis corpus for character segmentation, Chinese named entity recognition and sentence backbone analysis respectively, and the output vectors of the three networks are transferred to the multi-task parameter sharing layer network respectively. The multi-task parameter sharing layer network uses the fully connected neural network to splice and train the eigenvectors from the three tasks, and transfers the training results back to the input layer of the bi-directional LSTM neural network. After several cycles of iterative training, the result sequence with sentence backbone tagging information isoutput. The invention adopts the method of combining the artificial neural network based on the depth learning and the multi-task learning of the semantic elements in the sentence, which can improve the system accuracy, the reaction speed and the fault tolerance.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to a sentence stem analysis method and system of a multi-task deep neural network based on word segmentation and named entity recognition. Background technique [0002] Automatic analysis of sentence stems in text data is an important application field of artificial intelligence technologies such as natural language processing and syntactic analysis. Its main purpose is to apply natural language processing technology and machine learning technology to allow computers to automatically analyze sentences in digital texts. Analyze and output a structured representation of key information such as the subject, predicate, and object of the sentence. [0003] The basic idea of ​​the present invention is: first, use three bidirectional LSTM neural networks with conditional random field to carry out word segmentation, named entity recognition and sentence stem analysis res...

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