Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Cuadros, Montse"'
The widespread availability of Question Answering (QA) datasets in English has greatly facilitated the advancement of the Natural Language Processing (NLP) field. However, the scarcity of such resources for minority languages, such as Basque, poses a
Externí odkaz:
http://arxiv.org/abs/2404.12177
This paper introduces the first version of the NUBes corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish). The corpus is part of an on-going research and currently consists of 29,682 sentences obtained from anonymised health r
Externí odkaz:
http://arxiv.org/abs/2004.01092
Massive digital data processing provides a wide range of opportunities and benefits, but at the cost of endangering personal data privacy. Anonymisation consists in removing or replacing sensitive information from data, enabling its exploitation for
Externí odkaz:
http://arxiv.org/abs/2003.03106
Publikováno v:
In Artificial Intelligence In Medicine November 2023 145
Hate speech is commonly defined as any communication that disparages a target group of people based on some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic. Due to the massive
Externí odkaz:
http://arxiv.org/abs/1809.04444
Publikováno v:
Perez, N., Cuadros, M., & Rigau, G. (2018). Biomedical term normalization of EHRs with UMLS. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). ELRA
This paper presents a novel prototype for biomedical term normalization of electronic health record excerpts with the Unified Medical Language System (UMLS) Metathesaurus. Despite being multilingual and cross-lingual by design, we first focus on proc
Externí odkaz:
http://arxiv.org/abs/1802.02870
With the increase of online customer opinions in specialised websites and social networks, the necessity of automatic systems to help to organise and classify customer reviews by domain-specific aspect/categories and sentiment polarity is more import
Externí odkaz:
http://arxiv.org/abs/1705.07687
Publikováno v:
In Expert Systems With Applications January 2018 91:127-137
Autor:
Arranz, Victoria, Choukri, Khalid, Cuadros, Montse, García-Pablos, Aitor, Gianola, Lucie, Grouin, Cyril, Herranz, Manuel, Paroubek, Patrick, Zweigenbaum, Pierre
Publikováno v:
Proceedings of the LREC 2022 Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Language Resources (LEGAL-MDLR 2022)
Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Language Resources (LEGAL-MDLR 2022)
Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Language Resources (LEGAL-MDLR 2022), Jun 2022, Marseille, France. pp.64-72
Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Language Resources (LEGAL-MDLR 2022)
Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Language Resources (LEGAL-MDLR 2022), Jun 2022, Marseille, France. pp.64-72
International audience; This paper presents the outcomes of the MAPA project, a set of annotated corpora for 24 languages of the European Union and an open-source customisable toolkit able to detect and substitute sensitive information in text docume
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::24156ba2442966d15382d5f20dbfbd4d
https://hal.science/hal-03873042
https://hal.science/hal-03873042
Publikováno v:
RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Universidad de Alicante (UA)
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
This paper describes the first approach to Grammatical Error Correction for Spanish health records. We present a series of experiments using neural networks and data augmentation, achieving 70.89 F0.5 score. Resources designed for this task are intro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::8055e646e63e54a6ce4cf2fb15a75a46
https://hdl.handle.net/10045/114244
https://hdl.handle.net/10045/114244