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SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering

Recent advances have witnessed a trending application of transfer learning in a broad spectrum of natural language processing (NLP) tasks, including question answering (QA). Transfer learning allows a model to inherit domain knowledge obtained from an existing model that has been sufficiently pre-tr...

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Autores principales: Zhu, Xian, Chen, Yuanyuan, Gu, Yueming, Xiao, Zhifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8961296/
https://www.ncbi.nlm.nih.gov/pubmed/35360832
http://dx.doi.org/10.3389/fnbot.2022.773329
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author Zhu, Xian
Chen, Yuanyuan
Gu, Yueming
Xiao, Zhifeng
author_facet Zhu, Xian
Chen, Yuanyuan
Gu, Yueming
Xiao, Zhifeng
author_sort Zhu, Xian
collection PubMed
description Recent advances have witnessed a trending application of transfer learning in a broad spectrum of natural language processing (NLP) tasks, including question answering (QA). Transfer learning allows a model to inherit domain knowledge obtained from an existing model that has been sufficiently pre-trained. In the biomedical field, most QA datasets are limited by insufficient training examples and the presence of factoid questions. This study proposes a transfer learning-based sentiment-aware model, named SentiMedQAer, for biomedical QA. The proposed method consists of a learning pipeline that utilizes BioBERT to encode text tokens with contextual and domain-specific embeddings, fine-tunes Text-to-Text Transfer Transformer (T5), and RoBERTa models to integrate sentiment information into the model, and trains an XGBoost classifier to output a confidence score to determine the final answer to the question. We validate SentiMedQAer on PubMedQA, a biomedical QA dataset with reasoning-required yes/no questions. Results show that our method outperforms the SOTA by 15.83% and a single human annotator by 5.91%.
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spelling pubmed-89612962022-03-30 SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering Zhu, Xian Chen, Yuanyuan Gu, Yueming Xiao, Zhifeng Front Neurorobot Neuroscience Recent advances have witnessed a trending application of transfer learning in a broad spectrum of natural language processing (NLP) tasks, including question answering (QA). Transfer learning allows a model to inherit domain knowledge obtained from an existing model that has been sufficiently pre-trained. In the biomedical field, most QA datasets are limited by insufficient training examples and the presence of factoid questions. This study proposes a transfer learning-based sentiment-aware model, named SentiMedQAer, for biomedical QA. The proposed method consists of a learning pipeline that utilizes BioBERT to encode text tokens with contextual and domain-specific embeddings, fine-tunes Text-to-Text Transfer Transformer (T5), and RoBERTa models to integrate sentiment information into the model, and trains an XGBoost classifier to output a confidence score to determine the final answer to the question. We validate SentiMedQAer on PubMedQA, a biomedical QA dataset with reasoning-required yes/no questions. Results show that our method outperforms the SOTA by 15.83% and a single human annotator by 5.91%. Frontiers Media S.A. 2022-03-10 /pmc/articles/PMC8961296/ /pubmed/35360832 http://dx.doi.org/10.3389/fnbot.2022.773329 Text en Copyright © 2022 Zhu, Chen, Gu and Xiao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Zhu, Xian
Chen, Yuanyuan
Gu, Yueming
Xiao, Zhifeng
SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering
title SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering
title_full SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering
title_fullStr SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering
title_full_unstemmed SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering
title_short SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering
title_sort sentimedqaer: a transfer learning-based sentiment-aware model for biomedical question answering
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8961296/
https://www.ncbi.nlm.nih.gov/pubmed/35360832
http://dx.doi.org/10.3389/fnbot.2022.773329
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