Research on short text classification method based on semantic fusion and BiLSTM-CNN

Xuejing Ding; Yingying Mei

doi:10.1117/12.2640209

1 August 2022 Research on short text classification method based on semantic fusion and BiLSTM-CNN

Xuejing Ding, Yingying Mei

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 1225721 (2022) https://doi.org/10.1117/12.2640209
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

Aiming at the problems of short text length, less information contained in text and poor classification performance caused by easy interference of deep learning model, combined with the advantages of CNN and BiLSTM, a text classification method based on fusion semantics, attention mechanism and BiLSTM-CNN model is proposed. Firstly, the Skip-gram model in word2vec is used to map the words in the data and convert them into vectors with fixed dimensions as the input of the model. The local features of the text vector are extracted by CNN and the global features related to the text context are extracted by BiLSTM. The features extracted by the two complementary models are fused, It solves the problem that the single convolution neural network model ignores the semantic and grammatical information of words in the context, and effectively avoids the gradient disappearance or gradient dispersion of the traditional cyclic neural network. This paper makes an experimental analysis on R8 and Ohsumed datasets. The results show that the proposed feature fusion model effectively improves the accuracy of text classification.

Citation Download Citation

Xuejing Ding and Yingying Mei "Research on short text classification method based on semantic fusion and BiLSTM-CNN", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 1225721 (1 August 2022); https://doi.org/10.1117/12.2640209

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