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Entity relationship extracting system based on deep neural network

A technology of deep neural network and entity relationship, which is applied in the field of entity relationship extraction system, can solve problems such as lack, achieve high accuracy, good robustness, and eliminate ambiguity

Inactive Publication Date: 2017-06-16
成都数联铭品科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, there is a lack of corresponding automatic entity relationship extraction tools based on part of speech

Method used

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  • Entity relationship extracting system based on deep neural network
  • Entity relationship extracting system based on deep neural network
  • Entity relationship extracting system based on deep neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] Establish or store the word vector conversion module and the part-of-speech vector conversion module in the computer or server, and carry out training: such as figure 2 Shown: Select a large corpus, use the word segmentation tool to segment all the sentences in the corpus, and get the word segmentation results. For the word segmentation results of the corpus, Word Embedding technology is used to generate the N-dimensional word vector of each word (the size of N latitude is set according to the number of words contained in the corpus, that is, the scale of the corpus; in the case of a large corpus, In order to avoid the problem of sparse coding, dimensionality reduction can be performed, such as using a vector to represent each word, using continuous changing numbers in the vector), and then obtaining the word vector matrix Matrix1 of the words contained in the corpus, where each row vector of the matrix A word vector corresponding to a word in the corpus. In this step...

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PUM

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Abstract

The invention relates to the field of natural language processing, in particular to an entity relationship extracting system based on a deep neural network. Texts to be processed are input into the system, and the system achieves automatic entity relationship judgement and output; the system inputs word property incorporating characteristics into a convolutional neural network, wherein the convolutional neural network completes automatic characteristic extraction of information of words, word properties and entity positions with respect to extracting relationship and performs automatic classification of the entity relationship. Manual characteristic extraction is not needed, and the prediction efficiency and accuracy are higher. The system provides an automatic entity relationship extracting tool.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to an entity relationship extraction system based on a deep neural network. Background technique [0002] With the rapid development of the Internet, the Internet has become the main channel for people to obtain information, and the content of text data on the Internet is also showing an exponential growth trend. The text data on the Internet contains a wealth of information, which is very useful for us to build knowledge bases or knowledge graphs; however, the workload of manual knowledge extraction is extremely huge. If computers can understand and extract useful information, it will be very important. meaning. However, almost all text data on the Internet exists in the form of natural language, which is unstructured and cannot be directly processed by computers. In order to solve this problem, information extraction technology emerges as the times require. Information...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06F17/30G06N3/04G06N3/08
CPCG06F16/35G06N3/08G06F40/295G06F40/30G06N3/045
Inventor 罗强刘世林丁国栋练睿罗镇权闫俊杰
Owner 成都数联铭品科技有限公司
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