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Hybrid RNNs and USE for enhanced sequential sentence classification in biomedical paper abstracts.
- Source :
- Bulletin of Electrical Engineering & Informatics; Oct2024, Vol. 13 Issue 5, p3334-3344, 11p
- Publication Year :
- 2024
-
Abstract
- This research evaluates a number of hybrid recurrent neural network (RNN) architectures for classifying sequential sentences in biomedical abstracts. The architectures include long short-term memory (LSTM), bidirectional LSTM (BI-LSTM), gated recurrent unit (GRU), and bidirectional GRU (BIGRU) models, all of which are combined with the universal sentence encoder (USE). The investigation assesses their efficacy in categorizing sentences into predefined classes: background, objective, method, result, and conclusion. Each RNN variant is used with the pre-trained USE as word embeddings to find complex sequential relationships in biomedical text. Results demonstrate the adaptability and effectiveness of these hybrid architectures in discerning diverse sentence functions. This research addresses the need for improved literature comprehension in biomedicine by employing automated sentence classification techniques, highlighting the significance of advanced hybrid algorithms in enhancing text classification methodologies within biomedical research. [ABSTRACT FROM AUTHOR]
- Subjects :
- RECURRENT neural networks
DATA mining
LEXICAL access
MEDICAL research
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 20893191
- Volume :
- 13
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Bulletin of Electrical Engineering & Informatics
- Publication Type :
- Academic Journal
- Accession number :
- 180146331
- Full Text :
- https://doi.org/10.11591/eei.v13i5.8240