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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Song, Y | - |
| dc.contributor.author | Kim, E | - |
| dc.contributor.author | Lee, GG | - |
| dc.contributor.author | Yi, BK | - |
| dc.date.accessioned | 2016-04-01T02:10:20Z | - |
| dc.date.available | 2016-04-01T02:10:20Z | - |
| dc.date.created | 2009-03-18 | - |
| dc.date.issued | 2005-06-01 | - |
| dc.identifier.issn | 1367-4803 | - |
| dc.identifier.other | 2005-OAK-0000005144 | - |
| dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/24578 | - |
| dc.description.abstract | POSBIOTM-NER is a trainable biomedical named-entity recognition system. POSBIOTM-NER can be automatically trained and adapted to new datasets without performance degradation, using CRF (conditional random field) machine learning techniques and automatic linguistic feature analysis. Currently, we have trained our system on three different datasets. GENIA-NER was trained based on GENIA Corpus, GENE-NER based on BioCreative data and GPCR-NER based on our own POSBIOTM/NE corpus, respectively, which would be used in GPCR-related pathway extraction. | - |
| dc.description.statementofresponsibility | X | - |
| dc.language | English | - |
| dc.publisher | OXFORD UNIV PRESS | - |
| dc.relation.isPartOf | BIOINFORMATICS | - |
| dc.title | POSBIOTM-NER: a trainable biomedical named-entity recognition system | - |
| dc.type | Article | - |
| dc.contributor.college | 컴퓨터공학과 | - |
| dc.identifier.doi | 10.1093/bioinformatics/bti414 | - |
| dc.author.google | Song, Y | - |
| dc.author.google | Kim, E | - |
| dc.author.google | Lee, GG | - |
| dc.author.google | Yi, BK | - |
| dc.relation.volume | 21 | - |
| dc.relation.issue | 11 | - |
| dc.relation.startpage | 2794 | - |
| dc.relation.lastpage | 2796 | - |
| dc.contributor.id | 10103841 | - |
| dc.relation.journal | BIOINFORMATICS | - |
| dc.relation.index | SCI급, SCOPUS 등재논문 | - |
| dc.relation.sci | SCI | - |
| dc.collections.name | Journal Papers | - |
| dc.type.rims | ART | - |
| dc.identifier.bibliographicCitation | BIOINFORMATICS, v.21, no.11, pp.2794 - 2796 | - |
| dc.identifier.wosid | 000229441500034 | - |
| dc.date.tcdate | 2019-02-01 | - |
| dc.citation.endPage | 2796 | - |
| dc.citation.number | 11 | - |
| dc.citation.startPage | 2794 | - |
| dc.citation.title | BIOINFORMATICS | - |
| dc.citation.volume | 21 | - |
| dc.contributor.affiliatedAuthor | Lee, GG | - |
| dc.identifier.scopusid | 2-s2.0-20744451122 | - |
| dc.description.journalClass | 1 | - |
| dc.description.journalClass | 1 | - |
| dc.description.wostc | 10 | - |
| dc.type.docType | Article | - |
| dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
| dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
| dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
| dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
| dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
| dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
| dc.relation.journalResearchArea | Computer Science | - |
| dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
| dc.relation.journalResearchArea | Mathematics | - |
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- 이근배LEE, GARY GEUNBAE
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