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Language template construction method and device

A construction method and grammar technology, applied in the field of big data, can solve the problems of low accuracy of target relationship, language templates cannot reflect semantic information, etc.

Inactive Publication Date: 2018-03-06
北京深知无限人工智能科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the language template obtained only based on the grammatical dependency graph cannot reflect semantic information, so the accuracy of the extracted target relationship is low

Method used

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  • Language template construction method and device
  • Language template construction method and device
  • Language template construction method and device

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Embodiment Construction

[0064] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0065] see figure 1 , which is a flow chart of a method for constructing a language template provided by an embodiment of the present application.

[0066] The language template construction method provided by this embodiment includes the following steps:

[0067] S101: Acquire training text, and construct a grammar dependency graph based on the training text.

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Abstract

The embodiment of the invention discloses a language template construction method and a device, which achieve the aim on improving the target relation extraction accuracy. The method comprises the steps of acquiring a training text, and constructing a grammar dependency diagram based on the training text, wherein the grammar dependency diagram comprises vertexs in the training test and grammaticalrelations between the vertexs, and each vertex comprises words and / or word groups in the training text; determining a first vertex matched with a relation theory element and a second vertex semantically matched with a target relation in each vertex of the grammar dependency diagram; extracting at least one first grammar subgraph from the grammar dependency diagram, and forming a first grammar subgraph set, wherein each first grammar subgraph is a minimum grammar subgraph comprising the first vertex, the second vertex and a grammar relation between the first vertex and the second vertex; learning to generate a first language template set according to the first grammar subgraph in the first grammar subgraph set, wherein the first language template set comprises at least one first language template.

Description

technical field [0001] The present application relates to the field of big data, in particular to a language template construction method and device. Background technique [0002] With the continuous development of big data, how to use natural language processing and data mining related technologies to help users obtain valuable information from massive information is an urgent need for contemporary computer research technology, so the Relation Extraction technology came into being . The main purpose of Relation Extraction is to extract specific relationships from natural language texts, such as kinship relationships, acquisition relationships, etc., and entities with such relationships, such as people, objects, companies, etc. [0003] The relation extraction method roughly includes the following steps: first, define the target relation (target relation), that is, which relation arguments (relation arguments) constitute the target relation. Then, the training corpus is us...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27
CPCG06F40/279G06F40/30
Inventor 汉斯·乌思克尔特亚历山德拉·加布里斯萨克徐飞玉李宏塞巴斯蒂安·克劳泽
Owner 北京深知无限人工智能科技有限公司
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