A Method of Entity Relationship Extraction

An entity relationship and relationship technology, applied in the field of information processing, can solve problems such as high labor costs and inability to deal with Internet volume entities and relationship information.

Active Publication Date: 2020-12-01
PEKING UNIV SHENZHEN GRADUATE SCHOOL
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In today's information explosion, traditional supervised relationship extraction cannot cope with the huge and growing entity and relationship information on the Internet due to the high labor cost required to label training samples.

Method used

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  • A Method of Entity Relationship Extraction
  • A Method of Entity Relationship Extraction
  • A Method of Entity Relationship Extraction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0073] Please refer to figure 1 , a flowchart of an entity relationship extraction method. The entity relationship extraction methods disclosed in this application include:

[0074] S301. Input the preprocessing information into the word sequence neural network to extract the first type of extraction relationship. The preprocessing information includes several sentences, and the sentences can be obtained directly from text information or voice information. Extracting relationships includes outputting various relationships represented by preprocessing information and the probabilities of the respective relationships.

[0075] Query the entity referred to by each word in the preprocessing information based on the knowledge graph, and convert the preprocessing information into an entity sequence according to the order of the entities in the preprocessing information;

[0076] Segment the preprocessing information to obtain several words; convert the preprocessing information i...

Embodiment 2

[0100] Please refer to figure 2 and image 3 , figure 2 It is a flowchart of an entity relationship extraction method, image 3 It is a schematic diagram of the structure of an entity relationship extraction method. The entity relationship extraction method in this embodiment includes:

[0101] S401. Transform the preprocessed information into an entity sequence, specifically including:

[0102] Through n-gram (referring to n words that appear consecutively) text matching, the sequence of appearance of entities in the preprocessing information is linked to the knowledge graph used, while retaining the link of each entity to refer to the candidate entity. Wherein, entity linking (EntityLinking), or entity linking, refers to linking the name appearing in the preprocessing information to the entity it refers to. In natural language, multiple entities may share the same name, that is, names may be ambiguous. For example, the name "Washington" can refer not only to the firs...

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Abstract

An entity relationship extraction method, since the preprocessed information is input into the word sequence neural network and the entity sequence neural network, and the relationship is extracted separately, and the two networks learn from each other through two-way knowledge distillation, and integrate the two networks. The relationship prediction result is output as the final prediction result. Since the preprocessing information is input into two different neural networks, the two neural networks are trained at the same time, and each other acts as the teacher of the other to adjust the parameters of the neural network, and finally the extraction relationship output by the two neural networks is weighted and integrated, so that the two A neural network removes the noise data in the training samples in a collaborative manner, and integrates the respective advantages of two different neural networks to achieve the purpose of optimizing noise reduction.

Description

technical field [0001] The invention relates to the field of information processing, in particular to an entity relationship extraction method. Background technique [0002] Information Extraction refers to the process of extracting information such as entities, events, and relationships from a piece of text, forming structured data and storing it in a database for user query and use. Relation Extraction is the key content of information extraction, which aims to discover the semantic relationship between entities in the real world. In recent years, this technology has been widely used in many machine learning and natural language processing tasks, including the construction and completion of Knowledge Graph (KG), information retrieval, question answering system, etc. [0003] Traditional relation extraction research generally adopts supervised machine learning methods, which regard relation extraction as a classification problem, use manually labeled training data, and tra...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/36G06N3/04
CPCG06N3/045
Inventor 雷凯陈道源沈颖
Owner PEKING UNIV SHENZHEN GRADUATE SCHOOL
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