kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Dual sentence representation model integrating prior knowledge for bio-text-mining
Xi An Jiao Tong Univ, Shaanxi Prov Key Lab Satellite & Terr Network Tec, Sch Comp Sci & Technol, Xian, Peoples R China..
Xi An Jiao Tong Univ, Shaanxi Prov Key Lab Satellite & Terr Network Tec, Sch Comp Sci & Technol, Xian, Peoples R China..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
Xi An Jiao Tong Univ, Shaanxi Prov Key Lab Satellite & Terr Network Tec, Sch Comp Sci & Technol, Xian, Peoples R China..
Show others and affiliations
2020 (English)In: 2020 IEEE international conference on bioinformatics and biomedicine / [ed] Park, T Cho, YR Hu, X Yoo, I Woo, HG Wang, J Facelli, J Nam, S Kang, M, Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 2409-2416Conference paper, Published paper (Refereed)
Abstract [en]

Data mining, especially the extraction of the relationship between genes and proteins, plays an important role in the biomedical field. Several related models have been proposed for data mining in the biomedical domain. Furthermore, manually curated biomedical knowledge bases, which could assist the task, have been used to enhance the data-mining model. However, due to the limitation of methods, much prior knowledge information is not be fully exploited. In this work, we propose a novel method that reasonably applied the curated prior knowledge for biomedical text mining by dual sentence representation models; one model is for the experimental data and the other one is for the prior knowledge information sentence. We evaluated our method on two community-supported datasets; BioNLP and BioCreative corpora. The experimental results demonstrate that the dual sentence representation model can successfully utilize external prior knowledge information to extract relationship from biomedical text. Our method can achieve state-of-art results and it could be an application of biomedical relation extraction in the future.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2020. p. 2409-2416
Series
IEEE International Conference on Bioinformatics and Biomedicine-BIBM, ISSN 2156-1125
Keywords [en]
sentence representation, biological relation extraction, prior knowledge information
National Category
Computer Sciences Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-300027DOI: 10.1109/BIBM49941.2020.9313239ISI: 000659487102073Scopus ID: 2-s2.0-85100343983OAI: oai:DiVA.org:kth-300027DiVA, id: diva2:1587191
Conference
IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM), DEC 16-19, 2020, ELECTR NETWORK
Note

QC 20210824

Available from: 2021-08-24 Created: 2021-08-24 Last updated: 2023-04-05Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Chatterjee, Saikat

Search in DiVA

By author/editor
Chatterjee, Saikat
By organisation
Information Science and Engineering
Computer SciencesLanguage Technology (Computational Linguistics)

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 34 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf