Entity joint labeling relation extraction method and system based on probabilistic graph

A technology of relation extraction and entity extraction, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as failure to consider the correlation of two subtasks, error accumulation, and inability to provide better solutions in series

CN114298052AActive Publication Date: 2022-04-08NAT UNIV OF DEFENSE TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2022-04-08

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Abstract

The invention discloses an entity joint labeling relation extraction method and system based on a probability graph, and belongs to the technical field of natural language processing. Comprising the steps of feature extraction; an entity extraction task is converted into a sequence labeling task, a sequence is input into a first model to obtain a first output feature, a prediction sequence is obtained after the first output feature is activated, and the starting position and the ending position of the entity are obtained through a set threshold value; the subject and the object are matched according to the principle of proximity, and similar entity heads and tails are marked and intercepted; and performing relationship classification: randomly extracting entity pairs, generating second output features according to the intermediate features of the first model, and inputting the second output features into a second model to obtain a corresponding classification relationship. According to the method, the correlation between the two sub-tasks is considered, so that a task extraction result does not excessively depend on an entity extraction result, and the problem of error accumulation and relation overlapping are avoided.
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Description

technical field

[0001] The invention belongs to the technical field of natural language processing, and in particular relates to a method and system for extracting entity joint annotation relations based on probability graphs. Background technique

[0002] Relation extraction is an important sub-task of information extraction. The purpose is to extract structured data from unstructured text. The main task of relation extraction is to extract the entities in the text and the relationships between entities. These relationships are based on three Formal representation of tuples (subject, relation, object), which plays an important role in building knowledge graphs. The existing mainstream relation extraction techniques are divided into rule-based relation extraction, supervised relation extraction, unsupervised relation extraction and semi-supervised relation extraction.

[0003] Rule-based relation extraction firstly extracts relational words based on rules and artificially, ...

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with embodiment and accompanying drawing. It should be noted that the terms "first", "second" and so on are only for convenience of description, and should not be construed as limitations on quantity, property and the like.

[0048] figure 1 A flow chart of the method for extracting entity joint annotation relations based on probability graphs is shown in the present invention. combine figure 1 As shown, the method in the embodiment of the present invention includes the following steps:

[0049] Step S100: Receive the text to be extracted from the entity joint labeling relationship; after receiving the text data, it is usually necessary to remove abnormal values ​​in the text, such as punctuation, URL links, etc.

[0050] Step S200: Perform feature extraction: use the pre-trained BERT encoder to generate the text embedding, perform word-word mixed encoding and position encoding on the word embedding, ...