Entity relationship extracting method based on deep neural network

Abstract

The invention discloses an entity relationship extracting method based on a deep neural network. The entity relationship extracting method comprises the following steps of: mapping each word or class keyword of a sentence to a word vector or a class vector respectively; carrying out characteristic extraction on the sentence according to the word vector and the class vector; and connecting extracted characteristics end to end and inputting into a whole-connection classification layer to obtain an extracted result. An entity relationship of a text is extracted by utilizing a common neural network and a convolutional neural network in a machine learning process, the accuracy and performance of the extraction of the entity relationship are improved and the artificial workload in the extraction of the entity relationship is simplified. The pre-trained word vector is utilized and the convergence rate and accuracy of the neural network are improved; and sentence characteristics and class characteristics are introduced and the convolutional neural network and the common neural network are used for extracting, so that the problem of long and short sentences is solved and the performance of the extraction of the entity relationship is improved.

Description

technical field [0001] The invention relates to machine learning and natural language processing, in particular to an entity relationship extraction method based on a deep neural network. Background technique [0002] Human-like intelligence is developing rapidly, and corresponding products have been used in education, medical and other industries, such as the Japanese Todai Robot project for primary education examination questions and the American IBH Watson project dedicated to intelligence question answering and expanding to the medical field. In China, the basic educational resources represented by "Wenzong" contain a wealth of knowledge, and a complete and high-quality knowledge base determines the intelligence level of a human-like intelligent question answering system. [0003] Deep learning technology has made major breakthroughs in the field of images and speech recognition, and has also been in full swing in the field of natural language. Among these deep learning...

AI Technical Summary

Problems solved by technology

[0004] At present, the construction of Chinese knowledge base still mainly has problems such as low utilization rate of unstructured text and low coverage of entities in specific fields. Complex language features, few public corpora for Chinese relationship extraction, complex preprocessing for Chinese relationship extraction, etc., increase the difficulty of Chinese entity relationship extraction

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