Hybridization model of frame semantics and deep learning for text semantic similarity calculation

Haijing Liu

doi:10.1117/12.3004103

11 October 2023 Hybridization model of frame semantics and deep learning for text semantic similarity calculation

Haijing Liu

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128001C (2023) https://doi.org/10.1117/12.3004103
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Text semantic similarity computation is a fundamental problem in the field of natural language processing. In recent years, text semantic similarity algorithms based on deep learning have become the mainstream research paradigm, but they suffer from the problem of insufficient understanding of text semantics and thus unclear interpretation of computational results. In this paper, we propose a text semantic encoding model combining frame semantic theory and deep learning, which enriches the semantic representation within sentences by making full use of the frame, frame elements, lexical units, and inter-frame relations in the frame semantic knowledge base and combining them with the Bert model, and interacts with the semantics between sentences through Siamese transformer encoders. The experiments are validated on MSRParaphraseCorpus and quora-question-pairs, and the results show that the model proposed in this paper outperforms similar models in terms of F1 values.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haijing Liu "Hybridization model of frame semantics and deep learning for text semantic similarity calculation", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128001C (11 October 2023); https://doi.org/10.1117/12.3004103

ACCESS THE FULL ARTICLE

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