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.
Rest-Mex: Thematic Unsupervised Classification IberLEF 2023
- NLP topic: topic modeling
- Dataset: Rest-Mex 2023 Clustering
- Forum: IberLEF
- Competition: Rest-Mex 2023: Research on Sentiment Analysis Task for Mexican Tourist Texts
- Domain: News
- Language(s): Spanish
MEDDOPLACE: Location Entity Recognition IberLEF 2023
- NLP topic: (named) entity recognition
- Dataset: MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction
- Forum: IberLEF
- Competition: MEDDOPLACE: MEDical DOcument PLAce-related Content Extraction
- Domain: Health
- Language(s): Spanish
GUA-SPA: Named entity classification IberLEF 2023
- NLP topic: (named) entity recognition
- Dataset: GUA-SPA: Guarani Spanish corpus
- Forum: IberLEF
- Competition: GUA-SPA: Guarani-Spanish Code-Switching Analysis
- Domain: News
- 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
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
Named entity recognition IberLEF 2020
- NLP topic: (named) entity recognition
- Dataset: CAPITEL-NER
- Forum: IberLEF
- Competition: Named entity recognition and Universal Dependency parsing
- Domain:
- Language(s): Spanish
Entity classification IberLEF 2019
- NLP topic: (named) entity recognition
- Dataset: MEDDOCAN
- Forum: IberLEF
- Competition: MEDDOCAN: Medical Document Anonymization Track
- 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.
