Entity relation joint extraction method and system based on active deep learning

An entity-relationship and active-depth technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as high labeling costs and lack of samples for domain text data labeling, so as to avoid error accumulation, solve overlapping relationship problems, The effect of reducing labor costs

Pending Publication Date: 2022-01-07
NORTHEASTERN UNIV
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Problems solved by technology

[0007] The technical problem to be solved by the present invention is to address the deficiencies of the above-mentioned existing technologies, and specifically address the problem of lack of labeled samples of text data in the field and the high cost of labeling. The present invention proposes a joint entity relationship extraction method and system based on active deep learning , realize the joint extraction of entity relations

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  • Entity relation joint extraction method and system based on active deep learning
  • Entity relation joint extraction method and system based on active deep learning
  • Entity relation joint extraction method and system based on active deep learning

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[0072] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0073] In this embodiment, the aviation field is taken as an example, and the entity relationship joint extraction method based on active deep learning of the present invention is used to jointly extract entity relations in the aviation field.

[0074] In this embodiment, an entity-relationship joint extraction method based on active deep learning, such as figure 1 shown, including the following steps:

[0075] Step 1: Obtain the data set to be labeled as a corpus; process the data set to be labeled into segments and sentences, and obtain the data set U to be labeled with sentences as the unit as a corpus;

[0076] In this embodiment, use OCR technology to convert PDF fo...

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Abstract

The invention provides an entity relation joint extraction method and system based on active deep learning, and relates to the technical field of computer natural language processing. The method comprises the following steps: firstly, acquiring a to-be-labeled sample data set as a corpus, performing concept extraction on the corpus, and defining an entity category set and a relationship category set; carrying out sample sampling by using a to-be-labeled sampling method based on active learning to obtain a to-be-labeled sample data set; performing data enhancement on the to-be-labeled sample data set by using an improved EDA method; then, according to the defined entity and relationship category set, labeling data of the to-be-labeled sample data set by adopting a BIO-OVE/R-HT labeling strategy; and finally, inputting the labeled data into an entity relationship joint extraction model for training; and when the model is used for prediction, decoding the predicted label by using a decoding rule corresponding to the labeling strategy to obtain a triple. According to the system, the entity relationship is extracted, and meanwhile, the extracted entity relationship is used for quickly constructing a knowledge graph and managing the knowledge graph.

Description

technical field [0001] The present invention relates to the technical field of computer natural language processing, in particular to an entity-relationship joint extraction method and system based on active deep learning. Background technique [0002] Entity relationship extraction is to extract the relationship between entities and entities from text. In order to solve the problem of entity relationship extraction, people have proposed a variety of methods, which can be roughly divided into two categories: pipeline extraction model and joint extraction model. [0003] The first is to divide the entity relationship extraction into two subtasks, entity recognition and relationship extraction, and the two subtasks are executed sequentially without interaction. The Chinese patent "CN113297838A A Relational Extraction Method Based on Graph Neural Network" uses the idea of ​​the first pipeline extraction model. This patent performs data processing on the document to be extract...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/295G06F40/247G06F40/194G06K9/62G06N3/08G06F16/36G06F16/35
CPCG06F40/295G06F16/35G06F40/247G06N3/08G06F16/367G06F40/194G06F18/214
Inventor 刘珂靳显鑫冷芳玲鲍玉斌于戈
Owner NORTHEASTERN UNIV
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