NCU-IISR/AS-GIS: Results of various pre-trained biomedical language models and linear regression model in BioASQ task 9b Phase B

Yu Zhang; Jen Chieh Han; Richard Tzong Han Tsai

NCU-IISR/AS-GIS: Results of various pre-trained biomedical language models and linear regression model in BioASQ task 9b Phase B

Yu Zhang, Jen Chieh Han, Richard Tzong Han Tsai

資訊工程學系

研究成果: 雜誌貢獻 › 會議論文 › 同行評審

2 引文斯高帕斯（Scopus）

摘要

Transformer has been widely applied in Natural Language Processing (NLP) field, and it also results in an amount of pre-trained language models like BioBERT, SciBERT, NCBI_Bluebert, and PubMedBERT. In this paper, we introduce our system for the BioASQ Task 9b Phase B. We employed various pre-trained biomedical language models, including BioBERT, BioBERT-MNLI, and PubMedBERT, to generate “exact” answers for the questions, and a linear regression model with our sentence embedding to construct the top-n sentences as a prediction for “ideal” answers.

原文	???core.languages.en_GB???
頁（從 - 到）	360-368
頁數	9
期刊	CEUR Workshop Proceedings
卷	2936
出版狀態	已出版 - 2021
事件	2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021 - Virtual, Bucharest, Romania 持續時間: 21 9月 2021 → 24 9月 2021

用於藥物推薦和併發症總結的臨床問答技術(1/3)
Tsai, T.-H. (PI)
1/08/20 → 31/07/21
研究計畫: Research

引用此

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abstract = "Transformer has been widely applied in Natural Language Processing (NLP) field, and it also results in an amount of pre-trained language models like BioBERT, SciBERT, NCBI_Bluebert, and PubMedBERT. In this paper, we introduce our system for the BioASQ Task 9b Phase B. We employed various pre-trained biomedical language models, including BioBERT, BioBERT-MNLI, and PubMedBERT, to generate “exact” answers for the questions, and a linear regression model with our sentence embedding to construct the top-n sentences as a prediction for “ideal” answers.",

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author = "Yu Zhang and Han, {Jen Chieh} and Tsai, {Richard Tzong Han}",

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T2 - 2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021

AU - Zhang, Yu

AU - Han, Jen Chieh

AU - Tsai, Richard Tzong Han

PY - 2021

Y1 - 2021

N2 - Transformer has been widely applied in Natural Language Processing (NLP) field, and it also results in an amount of pre-trained language models like BioBERT, SciBERT, NCBI_Bluebert, and PubMedBERT. In this paper, we introduce our system for the BioASQ Task 9b Phase B. We employed various pre-trained biomedical language models, including BioBERT, BioBERT-MNLI, and PubMedBERT, to generate “exact” answers for the questions, and a linear regression model with our sentence embedding to construct the top-n sentences as a prediction for “ideal” answers.

AB - Transformer has been widely applied in Natural Language Processing (NLP) field, and it also results in an amount of pre-trained language models like BioBERT, SciBERT, NCBI_Bluebert, and PubMedBERT. In this paper, we introduce our system for the BioASQ Task 9b Phase B. We employed various pre-trained biomedical language models, including BioBERT, BioBERT-MNLI, and PubMedBERT, to generate “exact” answers for the questions, and a linear regression model with our sentence embedding to construct the top-n sentences as a prediction for “ideal” answers.

KW - Biomedical question answering

KW - Linear regression

KW - Pre-trained language model

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JF - CEUR Workshop Proceedings

Y2 - 21 September 2021 through 24 September 2021

ER -

NCU-IISR/AS-GIS: Results of various pre-trained biomedical language models and linear regression model in BioASQ task 9b Phase B

摘要

指紋

專案

用於藥物推薦和併發症總結的臨床問答技術(1/3)

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