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Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system

BACKGROUND: When users inquire about knowledge in a certain field using the internet, the intelligent question-answering system based on frequently asked questions (FAQs) provides numerous concise and accurate answers that have been manually verified. However, there are few specific question-answeri...

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Autores principales: Li, Meiting, Shen, Xifeng, Sun, Yuanyuan, Zhang, Weining, Nan, Jiale, Zhu, Jia’an, Gao, Dongping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102790/
https://www.ncbi.nlm.nih.gov/pubmed/37064382
http://dx.doi.org/10.21037/qims-22-749
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author Li, Meiting
Shen, Xifeng
Sun, Yuanyuan
Zhang, Weining
Nan, Jiale
Zhu, Jia’an
Gao, Dongping
author_facet Li, Meiting
Shen, Xifeng
Sun, Yuanyuan
Zhang, Weining
Nan, Jiale
Zhu, Jia’an
Gao, Dongping
author_sort Li, Meiting
collection PubMed
description BACKGROUND: When users inquire about knowledge in a certain field using the internet, the intelligent question-answering system based on frequently asked questions (FAQs) provides numerous concise and accurate answers that have been manually verified. However, there are few specific question-answering systems for chronic diseases, such as rheumatoid arthritis, and the related technology to construct a question-answering system about chronic diseases is not sufficiently mature. METHODS: Our research embedded the classification information of the question into the sentence vector based on the bidirectional encoder representations from transformers (BERT) language model. First of all, we calculated the similarity using edit distance to recall the candidate set of similar questions. Then, we took advantage of the BERT pretraining model to map the sentence information to the corresponding embedding representation. Finally, each dimensional feature of the sentence was obtained by passing a sentence vector through the multihead attention layer and the fully connected feedforward layer. The features that were stitched and fused were used for the semantic similarity calculation. RESULTS: Our improved model achieved a Top-1 precision of 0.551, Top-3 precision of 0.767, and Top-5 precision of 0.813 on 176 testing question sentences. In the analysis of the actual application effect of the model, we found that our model performed well in understanding the actual intention of users. CONCLUSIONS: Our deep learning model takes into account the background and classifications of questions and combines the efficiency of deep learning technology and the comprehensibility of semantics. The model enables the deep meaning of the user’s question to be better understood by the intelligent question answering system, and answers that are more relevant to the original query are provided.
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spelling pubmed-101027902023-04-15 Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system Li, Meiting Shen, Xifeng Sun, Yuanyuan Zhang, Weining Nan, Jiale Zhu, Jia’an Gao, Dongping Quant Imaging Med Surg Original Article BACKGROUND: When users inquire about knowledge in a certain field using the internet, the intelligent question-answering system based on frequently asked questions (FAQs) provides numerous concise and accurate answers that have been manually verified. However, there are few specific question-answering systems for chronic diseases, such as rheumatoid arthritis, and the related technology to construct a question-answering system about chronic diseases is not sufficiently mature. METHODS: Our research embedded the classification information of the question into the sentence vector based on the bidirectional encoder representations from transformers (BERT) language model. First of all, we calculated the similarity using edit distance to recall the candidate set of similar questions. Then, we took advantage of the BERT pretraining model to map the sentence information to the corresponding embedding representation. Finally, each dimensional feature of the sentence was obtained by passing a sentence vector through the multihead attention layer and the fully connected feedforward layer. The features that were stitched and fused were used for the semantic similarity calculation. RESULTS: Our improved model achieved a Top-1 precision of 0.551, Top-3 precision of 0.767, and Top-5 precision of 0.813 on 176 testing question sentences. In the analysis of the actual application effect of the model, we found that our model performed well in understanding the actual intention of users. CONCLUSIONS: Our deep learning model takes into account the background and classifications of questions and combines the efficiency of deep learning technology and the comprehensibility of semantics. The model enables the deep meaning of the user’s question to be better understood by the intelligent question answering system, and answers that are more relevant to the original query are provided. AME Publishing Company 2023-03-15 2023-04-01 /pmc/articles/PMC10102790/ /pubmed/37064382 http://dx.doi.org/10.21037/qims-22-749 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Li, Meiting
Shen, Xifeng
Sun, Yuanyuan
Zhang, Weining
Nan, Jiale
Zhu, Jia’an
Gao, Dongping
Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system
title Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system
title_full Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system
title_fullStr Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system
title_full_unstemmed Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system
title_short Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system
title_sort using semantic text similarity calculation for question matching in a rheumatoid arthritis question-answering system
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102790/
https://www.ncbi.nlm.nih.gov/pubmed/37064382
http://dx.doi.org/10.21037/qims-22-749
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