Ms. Shuang Pan Profile

Shuang Pan

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 29 April 2023 Paper

Few-shot relation extraction based on multi-level feature metric learning

YaXin Fang, ZhongLin Liu, Shuang Pan, Qian Li

Proceedings Volume 12610, 1261058 (2023) https://doi.org/10.1117/12.2671059

KEYWORDS: Feature extraction, Education and training, Prototyping, Semantics, Transformers, Matrices, Convolution

Read Abstract +

Relationship extraction is an important task in natural language processing and knowledge mapping. Traditional entity relationship extraction methods have achieved high accuracy in practical applications. However, when faced with the task of entity relationship extraction that is not easy to obtain large-scale supervision training data sets, traditional methods cannot get good results. In this paper, a few-shot relation extraction method based on multi-level feature metric learning is proposed. This method takes the prototype network as the baseline network to generate a class prototype. Firstly, a multi-level feature extraction module is proposed. This module combines the multi-level features of the text with the multi-level attention mechanism, which can fully extract the features of the text. Secondly, a loss function based on label value and negative sample distance is proposed. This algorithm introduces an evaluation mechanism of negative sample distance on the basis of the prototype network, so that the model can adaptively allocate parameters and improve the clustering ability of small samples. Experiments are conducted on FewRel1.0 which is a small sample relational data set. Experiment results show that compared with other models, our model can improve classification accuracy.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years