C-Norm: a neural approach to few-shot entity normalization
Abstract Background Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-020-03886-8 |
| _version_ | 1818601586888278016 |
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| author | Arnaud Ferré Louise Deléger Robert Bossy Pierre Zweigenbaum Claire Nédellec |
| author_facet | Arnaud Ferré Louise Deléger Robert Bossy Pierre Zweigenbaum Claire Nédellec |
| author_sort | Arnaud Ferré |
| collection | DOAJ |
| description | Abstract Background Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have difficulty handling highly multi-class and few-shot learning problems. To address this issue, we propose C-Norm, a new neural approach which synergistically combines standard and weak supervision, ontological knowledge integration and distributional semantics. Results Our approach greatly outperforms all methods evaluated on the Bacteria Biotope datasets of BioNLP Open Shared Tasks 2019, without integrating any manually-designed domain-specific rules. Conclusions Our results show that relatively shallow neural network methods can perform well in domains that present highly multi-class and few-shot learning problems. |
| first_indexed | 2024-12-16T12:53:45Z |
| format | Article |
| id | doaj.art-88f113a8208d4915abbd31ee9b1d0d68 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-16T12:53:45Z |
| publishDate | 2020-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-88f113a8208d4915abbd31ee9b1d0d682022-12-21T22:31:04ZengBMCBMC Bioinformatics1471-21052020-12-0121S2311910.1186/s12859-020-03886-8C-Norm: a neural approach to few-shot entity normalizationArnaud Ferré0Louise Deléger1Robert Bossy2Pierre Zweigenbaum3Claire Nédellec4Université Paris-Saclay, INRAE, MaIAGEUniversité Paris-Saclay, INRAE, MaIAGEUniversité Paris-Saclay, INRAE, MaIAGEUniversité Paris-Saclay, CNRS, LIMSIUniversité Paris-Saclay, INRAE, MaIAGEAbstract Background Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have difficulty handling highly multi-class and few-shot learning problems. To address this issue, we propose C-Norm, a new neural approach which synergistically combines standard and weak supervision, ontological knowledge integration and distributional semantics. Results Our approach greatly outperforms all methods evaluated on the Bacteria Biotope datasets of BioNLP Open Shared Tasks 2019, without integrating any manually-designed domain-specific rules. Conclusions Our results show that relatively shallow neural network methods can perform well in domains that present highly multi-class and few-shot learning problems.https://doi.org/10.1186/s12859-020-03886-8Entity normalizationNeural networksOntologyFew-shot learningVector space model |
| spellingShingle | Arnaud Ferré Louise Deléger Robert Bossy Pierre Zweigenbaum Claire Nédellec C-Norm: a neural approach to few-shot entity normalization BMC Bioinformatics Entity normalization Neural networks Ontology Few-shot learning Vector space model |
| title | C-Norm: a neural approach to few-shot entity normalization |
| title_full | C-Norm: a neural approach to few-shot entity normalization |
| title_fullStr | C-Norm: a neural approach to few-shot entity normalization |
| title_full_unstemmed | C-Norm: a neural approach to few-shot entity normalization |
| title_short | C-Norm: a neural approach to few-shot entity normalization |
| title_sort | c norm a neural approach to few shot entity normalization |
| topic | Entity normalization Neural networks Ontology Few-shot learning Vector space model |
| url | https://doi.org/10.1186/s12859-020-03886-8 |
| work_keys_str_mv | AT arnaudferre cnormaneuralapproachtofewshotentitynormalization AT louisedeleger cnormaneuralapproachtofewshotentitynormalization AT robertbossy cnormaneuralapproachtofewshotentitynormalization AT pierrezweigenbaum cnormaneuralapproachtofewshotentitynormalization AT clairenedellec cnormaneuralapproachtofewshotentitynormalization |