Relation extraction method suitable for small samples

A relationship extraction, small sample technology, applied in the field of knowledge graph construction, can solve problems such as inescapability, avoid time-consuming, money-consuming, and reduce manual labeling of data.

Pending Publication Date: 2020-05-08
南京中新赛克科技有限责任公司
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But for a variety of relationship types, in dealing with the relationship extraction problem in a specific field, even if the Bert model ca...

Method used

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  • Relation extraction method suitable for small samples
  • Relation extraction method suitable for small samples
  • Relation extraction method suitable for small samples

Examples

Experimental program
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Embodiment Construction

[0028] A method of relation extraction adapted to small samples, comprising the following steps:

[0029] (1) Obtain training data;

[0030] (2) General Domain Relational Knowledge Model Training;

[0031] (3) Domain-specific relationship extraction model training.

[0032] In step (1), the training data is acquired specifically as follows: the training data comes from two parts, one is public relational labeling data, and the other is training data generated based on weak supervision;

[0033] (11) Use crawler tools to collect non-formatted text data on the Internet;

[0034] (12) Obtain triplet data (including relationship name and entity pair) on the public dataset Freebase;

[0035] (13) Obtain entity pair and its corresponding sentence by the named entity recognition method of NLP by text data;

[0036] (14) Using the remote supervision method, the entity pair and its corresponding sentence are assigned a relationship, and the same entity pair and its sentence are pla...

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Abstract

The invention discloses a relation extraction method suitable for small samples. The relation extraction method comprises the following steps: (1) obtaining training data; (2) training a general domain relation knowledge model; and (3) training a specific domain relation extraction model. Common knowledge contained in various relationships is obtained by utilizing a general domain relationship knowledge module, and samples are automatically generated based on remote supervision by utilizing an open-source knowledge graph and combining with unsupervised noise reduction data to train relationship knowledge models of general and specific domains; a general domain relation knowledge module is adopted to learn general knowledge contained in various relations; training samples are automaticallygenerated on the basis of remote supervision, and noise data is reduced in combination with unsupervised data, so that manual annotation data is reduced; when the relation knowledge model is generated, a large amount of manual marking data does not need to be obtained, time and money consumption caused by a large amount of manual marking is avoided, and a relation extraction task in a specific field can be completed through a small amount of marking data in the specific field.

Description

technical field [0001] The present invention relates to the technical field of knowledge map construction, in particular to a method for extracting relationships adapted to small samples. Background technique [0002] Information extraction is an important part of natural language processing, especially in today's information society, it is particularly meaningful to extract useful information from massive data. Information extraction can be divided into entity extraction, relationship extraction and event extraction. The relationship extraction is based on the extracted entity pairs to determine whether there is a certain relationship between the entity pairs, and no relationship is also regarded as a special relationship. [0003] As relational extraction shifts from limited relational types to various relational types in the open domain, data sources shift from standard corpora to massive network data. Traditional pattern-matching-based methods cannot adapt to multiple ...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/35G06F16/951G06F40/295G06F40/211G06K9/62G06N3/04G06N3/08
CPCG06F16/367G06F16/35G06F16/951G06N3/088G06N3/045G06F18/23
Inventor 卓可秋杨秀燕
Owner 南京中新赛克科技有限责任公司
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