Method for extracting relations between entities from text based on self-supervision and clustering technologies

An adaptive clustering, entity technology, applied in the field of machine learning, can solve problems such as unsupervised, achieve good results, improve clustering purity, and prevent the effect of feature space

Active Publication Date: 2021-03-12
TIANJIN UNIV
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  • Abstract
  • Description
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  • Application Information

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[0009] (3) Unsupervised method

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  • Method for extracting relations between entities from text based on self-supervision and clustering technologies
  • Method for extracting relations between entities from text based on self-supervision and clustering technologies
  • Method for extracting relations between entities from text based on self-supervision and clustering technologies

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Embodiment Construction

[0049] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0050] Such as figure 1 As shown, the method for extracting the relationship between entities from text based on self-supervision and clustering technology proposed by the present invention includes three modules: context encoding module, clustering module and classification module.

[0051] 1. Context Encoding Module

[0052] The purpose of the context encoding module is to perform vector representations of two entities in a sentence. In the present invention, it is assumed that the entities in the known sentence have been labeled, and the present invention only focuses on the relationship between two entities. The relationship between a pair of entities must be associated with thei...

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Abstract

The invention discloses a method for extracting relations between entities from a text based on self-supervision and clustering technologies, and the method comprises the steps: carrying out the relation extraction in a label-free data set based on the idea of self-supervision. A context encoding module, a clustering module and a classification module are included. The context encoding module is used for encoding the data and then performing relationship extraction and analysis; the clustering module is divided into two parts of adaptive clustering of entities and adaptive clustering of entitycategories, wherein the adaptive clustering of the entities is used for directly clustering the entity codes, and the obtained clustering result is an entity category, wherein the self-adaptive clustering of the entity categories is to cluster the entity categories, and the obtained result is the relationship between the entity categories; and the classification module is used for linking all themodules for joint training.

Description

technical field [0001] The invention belongs to the field of machine learning and relates to text mining and information extraction, in particular to a method for extracting the relationship between entities from text based on self-supervision and clustering technology. Background technique [0002] With the emergence and development of the Internet, a large amount of information appears on the Internet in various forms, such as news articles, research publications, blogs, forums, etc. How to extract relevant and important information from these text information has become the current mainstream research trend. The basic goal of Information Extraction (IE) [1] is to extract a specific kind of information from a given document repository and output it into a structured repository [2]. Relation Extraction (RE) plays a key role in Information Extraction (IE), which aims to extract semantic relations between entity pairs in natural language sentences [3]. When building a knowl...

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Application Information

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IPC IPC(8): G06F16/35G06F40/211G06F40/284G06F40/289G06F40/30
CPCG06F16/355G06F40/211G06F40/289G06F40/284G06F40/30
Inventor 王鑫王博蒋沁学陈根华黄博帆
Owner TIANJIN UNIV
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