Tasks
A task is an activity proposed with the purpose of solving a specific NLP problem, generally within the framework of a competition. Below is information about NLP tasks in Spanish from 2013 to the present.
SpRadIE - Named entity recognition CLEF 2021
- NLP topic: (named) entity recognition
- Dataset: SpRadIE
- Forum: CLEF
- Competition: SpRadIE: A challenge on information extraction from Spanish Radiology Reports
- Domain: Health
- Language(s): Spanish (Argentina)
Occupation and occupation holder detection IberLEF 2021
- NLP topic: text classification
- Dataset: MEDDOPROF
- Forum: IberLEF
- Competition: Recognition, classification and normalization of professions and occupations from medical texts
- Domain: Health
- Language(s): Spanish
- NLP topic: text indexing
- Dataset: MESINESP8-L
- Forum: CLEF
- Competition: BioASQ 2021: Large-scale biomedical semantic indexing and question answering
- Domain: Health
- Language(s): Spanish
Named entity normalization IberLEF 2021
- NLP topic: text classification
- Dataset: MEDDOPROF
- Forum: IberLEF
- Competition: Recognition, classification and normalization of professions and occupations from medical texts
- Domain: Health
- Language(s): Spanish
Entity recognition IberLEF 2021
- NLP topic: (named) entity recognition
- Dataset: eHealth-KD-ES
- Forum: IberLEF
- Competition: eHealth Knowledge Discovery
- Domain: Health
- Language(s): Spanish, English
Automatic indexing of clinical trials CLEF 2021
- NLP topic: text indexing
- Dataset: MESINESP8-L
- Forum: CLEF
- Competition: BioASQ 2021: Large-scale biomedical semantic indexing and question answering
- Domain: Health
- Language(s): Spanish
Stance detection IberLEF 2021
- NLP topic: sentiment analysis
- Dataset: VaxxStance-ES
- Forum: IberLEF
- Competition: Stance detection about vaccines
- Domain: Health
- Language(s): Spanish
MEDDOPROF - Named entity recognition IberLEF 2021
- NLP topic: (named) entity recognition
- Dataset: MEDDOPROF
- Forum: IberLEF
- Competition: Recognition, classification and normalization of professions and occupations from medical texts
- Domain: Health
- Language(s): Spanish
Relation extraction IberLEF 2021
- NLP topic: relation extraction
- Dataset: eHealth-KD-ES
- Forum: IberLEF
- Competition: eHealth Knowledge Discovery
- Domain: Health
- Language(s): Spanish, English
Entity classification IberLEF 2019
- NLP topic: (named) entity recognition
- Dataset: MEDDOCAN
- Forum: IberLEF
- Competition: MEDDOCAN: Medical Document Anonymization Track
- Domain: Health
- Language(s): Spanish
Sensitive span detection IberLEF 2019
- NLP topic: information extraction
- Dataset: MEDDOCAN
- Forum: IberLEF
- Competition: MEDDOCAN: Medical Document Anonymization Track
- Domain: Health
- Language(s): Spanish
Detecting abbreviation-definition pairs IberEVAL 2018
- NLP topic: processing abbreviations
- Dataset: BARR2
- Forum: IberEVAL
- Competition: Biomedical Abbreviation Recognition and Resolution 2018
- Domain: Health
- Language(s): Spanish
Abbreviation resolution evaluation IberEVAL 2018
- NLP topic: processing abbreviations
- Dataset: BARR2
- Forum: IberEVAL
- Competition: Biomedical Abbreviation Recognition and Resolution 2018
- Domain: Health
- Language(s): Spanish
Abbreviation resolution IberEVAL 2018
- NLP topic: processing abbreviations
- Dataset: SPACCC
- Forum: IberEVAL
- Competition: Second Biomedical Abbreviation Recognition and Resolution (BARR2)
- Domain: Health
- Language(s): Spanish
DIANN 2018: Disability detection IberEVAL 2018
- NLP topic: (named) entity recognition
- Dataset: DIANN-2018-ES
- Forum: IberEVAL
- Competition: Disability annotation on documents from the biomedical domain (DIANN)
- Domain: Health
- Language(s): Spanish
Abbreviation recognition and resolution IberEVAL 2017
- NLP topic: processing abbreviations
- Dataset: BARR
- Forum: IberEVAL
- Competition: Biomedical Abbreviation Recognition and Resolution
- Domain: Health
- Language(s): Spanish
If you have published a result better than those on the list, send a message to odesia-comunicacion@lsi.uned.es indicating the result and the DOI of the article, along with a copy of it if it is not published openly.
