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Hybrid Self-Interactive Attentive Siamese Network for Medical Textual Semantic Similarity

Published: 19 May 2020 Publication History

Abstract

The rapid development of medicine produces a large number of medical texts, but it is difficult to process these texts due to many similar sentences. Therefore, estimating the similarity of medical texts has become a key technology, filtering out medical texts quickly. Nowadays, many methods for estimation similarity between medical sentences extract semantic features mainly via Siamese network. However; these methods don't achieve the best results due to the large amount of noise in the texts. To improve the performance of the Siamese network, a hybrid self-interactive attention model is proposed in this paper. The aim is to reduce the noise of the text and strengthen the token with high correlation between the two texts. In addition, this proposed model also uses BERT as the embedding layer to carry out a preliminary pre-training of text. Then, two datasets are employed to verify the effectiveness of our method and our method achieves better results on Pearson correlation coefficient, compared with the other existing methods. The experimental results still indicate that the results of pre-trained BERT are better than that of Word2Vec, and the hybrid self-interactive attention model obtains better results due to the effect of interactive attention.

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Cited By

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  • (2023)MIA-Net: Multi-Modal Interactive Attention Network for Multi-Modal Affective AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2023.325901014:4(2796-2809)Online publication date: 1-Oct-2023

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ICMSS 2020: Proceedings of the 2020 4th International Conference on Management Engineering, Software Engineering and Service Sciences
January 2020
301 pages
ISBN:9781450376419
DOI:10.1145/3380625
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • China University of Geosciences

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 May 2020

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Author Tags

  1. BERT
  2. Interactive attention
  3. Self-attention
  4. Siamese
  5. Textual Similarity

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  • (2023)MIA-Net: Multi-Modal Interactive Attention Network for Multi-Modal Affective AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2023.325901014:4(2796-2809)Online publication date: 1-Oct-2023

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