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.
- NLP topic: processing events
- Dataset: DA-VINCIS 2023
- Forum: IberLEF
- Competition: DA-VINCIS: Multimodal Information for the Detection of Aggressive and Violent INCIdents from Social media in Spanish
- Domain: Social
- Language(s): Spanish, Spanish (Mexico)
- NLP topic: processing events
- Dataset: DA-VINCIS 2023
- Forum: IberLEF
- Competition: DA-VINCIS: Multimodal Information for the Detection of Aggressive and Violent INCIdents from Social media in Spanish
- Domain: Social
- Language(s): Spanish, Spanish (Mexico)
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
Factuality classification IberLEF 2020
- NLP topic: processing factuality
- Dataset: FACT
- Forum: IberLEF
- Competition: FACT 2020: Factuality Analysis and Classification Task
- Domain:
- Language(s): Spanish
Event detection IberLEF 2020
- NLP topic: processing events
- Dataset: FACT
- Forum: IberLEF
- Competition: FACT 2020: Factuality Analysis and Classification Task
- Domain:
- Language(s): Spanish
Factuality classification IberLEF 2019
- NLP topic: processing factuality
- Dataset: FACT
- Forum: IberLEF
- Competition: FACT: Factuality Analysis and Classification Task,
- Domain:
- Language(s): Spanish
Classification of political topics IberEVAL 2017
- NLP topic: topic modeling
- Dataset: COSET
- Forum: IberEVAL
- Competition: 1st Classification of Spanish Election Tweets Task
- Domain: Politics
- 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.
