Joint extraction method for named entities and relationships in judicial domain

A technology of named entity and relationship extraction, applied in information extraction and judicial fields, can solve the problems of not considering overlapping relationships, error propagation, lost connections, etc.
CN113221567APending Publication Date: 2021-08-06北京航天情报与信息研究所
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
北京航天情报与信息研究所
Publication Date
2021-08-06

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Abstract

The invention discloses a joint extraction method for named entities and relationships in a judicial domain, which is an entity relation extraction method of a BILSTM network and an attention mechanism set based on a BERT pre-training language model, realizes joint learning of two tasks through parameter sharing, and fully utilizes the relation between the tasks to optimize a result. A BERT pre-training language model is selected to train word vectors to complete conversion work of the data set word vectors; more complete context feature information is acquired by using a BILSTM neural network so as to extract text depth word vector features; Finally, category labels of the characters are acquired through a softmax classifier to realize entity recognition, and an association relationship is judged between the current character and the previous character by utilizing an attention mechanism to realize combined extraction of the entity and multiple relationships.
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Description

technical field

[0001] The invention relates to the technical field of information extraction, and more specifically relates to a joint extraction method of named entities and relationships in the judicial field. Background technique

[0002] With the rapid development of the Internet and the explosive growth of information today, how to efficiently obtain the required information is a hot research issue, and information extraction technology has emerged as the times require. Information extraction can be subdivided into three subtasks: named entity recognition, entity relationship extraction, and event extraction. Obtaining semantic triples through entity recognition and entity relationship extraction is an important prerequisite for building knowledge graphs and understanding natural language. The judicial field is a typical knowledge-intensive industry. In the big data era of information explosion, laws and regulations, guiding cases, legal documents, etc. have emerged in...

Claims

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