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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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%. |
format | Online Article Text |
id | pubmed-8961296 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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