From LIMA to DeepLIMA: following a new path of interoperability.
In this article, we describe the architecture of the LIMA (Libre Multilingual Analyzer) framework and its recent evolution with the addition of new text analysis modules based on deep neural networks. We extended the functionality of LIMA in terms of the number of supported languages while preservin...
| Publicado en: | Language Resources & Evaluation Vol. 58; no. 4; pp. 1463 - 1481 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Springer Nature
Dec2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=180627313&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 180627313 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2024 vid: 58 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 180627313 10.1007/s10579-024-09773-5 ppf: 1463 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 919KB tig: atl: From LIMA to DeepLIMA: following a new path of interoperability. aug: au: Bocharov, Victor Besançon, Romaric de Chalendar, Gaël Ferret, Olivier Semmar, Nasredine affil: https://ror.org/03xjwb503 Université Paris-Saclay, CEA, List, F-91120, Palaiseau, France su: Artificial neural networks Natural language processing Universal language Deep learning Statistics sug: subj: Artificial neural networks Natural language processing Universal language Deep learning Statistics keyword: Interoperability Linguistic analyzer Neural models NLP platform Universal dependencies ab: In this article, we describe the architecture of the LIMA (Libre Multilingual Analyzer) framework and its recent evolution with the addition of new text analysis modules based on deep neural networks. We extended the functionality of LIMA in terms of the number of supported languages while preserving existing configurable architecture and the availability of previously developed rule-based and statistical analysis components. Models were trained for more than 60 languages on the Universal Dependencies 2.5 corpora, WikiNer corpora, and CoNLL-03 dataset. Universal Dependencies allowed us to increase the number of supported languages and generate models that could be integrated into other platforms. This integration of ubiquitous Deep Learning Natural Language Processing models and the use of standard annotated collections using Universal Dependencies can be viewed as a kind of model and data interoperability, complementary to the technical interoperability between systems. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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