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- Publisher Website: 10.1145/3459930.3469557
- Scopus: eid_2-s2.0-85112390963
- WOS: WOS:000722623700070
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Conference Paper: BioNumQA-BERT: Answering Biomedical Questions Using Numerical Facts with a Deep Language Representation Model
Title | BioNumQA-BERT: Answering Biomedical Questions Using Numerical Facts with a Deep Language Representation Model |
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Authors | |
Keywords | Text Mining Biomedical Question Answering BERT Numerical encoding |
Issue Date | 2021 |
Publisher | Association for Computing Machinery (ACM). |
Citation | The 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2021). Virtual Conference, 1-4 August 2021 How to Cite? |
Abstract | Biomedical question answering (QA) is playing an increasingly significant role in medical knowledge translation. However, current biomedical QA datasets and methods have limited capacity, as they commonly neglect the role of numerical facts in biomedical QA. In this paper, we constructed BioNumQA, a novel biomedical QA dataset that answers research questions using relevant numerical facts for biomedical QA model training and testing. To leverage the new dataset, we designed a new method called BioNumQA-BERT by introducing a novel numerical encoding scheme into the popular biomedical language model BioBERT to represent the numerical values in the input text. Our experiments show that BioNumQABERT significantly outperformed other state-of-art models, including DrQA, BERT and BioBERT (39.0% vs 29.5%, 31.3% and 33.2%, respectively, in strict accuracy). To improve the generalization ability of BioNumQA-BERT, we further pretrained it on a large biomedical text corpus and achieved 41.5% strict accuracy. BioNumQA and BioNumQA-BERT establish a new baseline for biomedical QA. The dataset, source codes and pretrained model of BioNumQA-BERT are available at https://github.com/LeaveYeah/BioNumQA-BERT. |
Description | BCB Session 6B: Ontologies & Databases |
Persistent Identifier | http://hdl.handle.net/10722/301148 |
ISBN | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wu, Y | - |
dc.contributor.author | Ting, HF | - |
dc.contributor.author | Lam, TW | - |
dc.contributor.author | Luo, R | - |
dc.date.accessioned | 2021-07-27T08:06:50Z | - |
dc.date.available | 2021-07-27T08:06:50Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | The 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2021). Virtual Conference, 1-4 August 2021 | - |
dc.identifier.isbn | 9781450384506 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301148 | - |
dc.description | BCB Session 6B: Ontologies & Databases | - |
dc.description.abstract | Biomedical question answering (QA) is playing an increasingly significant role in medical knowledge translation. However, current biomedical QA datasets and methods have limited capacity, as they commonly neglect the role of numerical facts in biomedical QA. In this paper, we constructed BioNumQA, a novel biomedical QA dataset that answers research questions using relevant numerical facts for biomedical QA model training and testing. To leverage the new dataset, we designed a new method called BioNumQA-BERT by introducing a novel numerical encoding scheme into the popular biomedical language model BioBERT to represent the numerical values in the input text. Our experiments show that BioNumQABERT significantly outperformed other state-of-art models, including DrQA, BERT and BioBERT (39.0% vs 29.5%, 31.3% and 33.2%, respectively, in strict accuracy). To improve the generalization ability of BioNumQA-BERT, we further pretrained it on a large biomedical text corpus and achieved 41.5% strict accuracy. BioNumQA and BioNumQA-BERT establish a new baseline for biomedical QA. The dataset, source codes and pretrained model of BioNumQA-BERT are available at https://github.com/LeaveYeah/BioNumQA-BERT. | - |
dc.language | eng | - |
dc.publisher | Association for Computing Machinery (ACM). | - |
dc.relation.ispartof | The 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB 2021) | - |
dc.subject | Text Mining | - |
dc.subject | Biomedical Question Answering | - |
dc.subject | BERT | - |
dc.subject | Numerical encoding | - |
dc.title | BioNumQA-BERT: Answering Biomedical Questions Using Numerical Facts with a Deep Language Representation Model | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Ting, HF: hfting@cs.hku.hk | - |
dc.identifier.email | Lam, TW: twlam@cs.hku.hk | - |
dc.identifier.email | Luo, R: rbluo@cs.hku.hk | - |
dc.identifier.authority | Ting, HF=rp00177 | - |
dc.identifier.authority | Lam, TW=rp00135 | - |
dc.identifier.authority | Luo, R=rp02360 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1145/3459930.3469557 | - |
dc.identifier.scopus | eid_2-s2.0-85112390963 | - |
dc.identifier.hkuros | 323502 | - |
dc.identifier.isi | WOS:000722623700070 | - |
dc.publisher.place | New York | - |