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  • A raw data set.
  • A learning task defined on existing data sets.
  • Describing a machine learning method.
  • Creating a challenge by grouping existing tasks.

Recent Items

  • Data realm-cnsm2015-vod-traces 2015-08-17 15:18
    Linux kernel statistics from a video-streaming cluster and service metrics from a video client
  • Data chemdner-patents-testset 2015-08-14 12:43
    chemdner patents test set text
  • Task GPRO patent protein recognition 2015-07-14 11:42
    GPRO: gene and protein related object mention recognition in patents task (CHEMDNER, BioCreative V)
  • Task CPD (chemical passage detection) 2015-07-14 11:36
    Participating teams have to classify patent titles and abstracts whether they do or do not contain mentions of chemical entities.
  • Task CEMP (chemical NER in patents) 2015-07-14 11:30
    CEMP (chemical entity mention in patents, main task): the detection of chemical named entity mentions in patents (start and end indices corresponding to all the chemical entities)

How does it work?

This repository manages the following types of objects.
  • Data Sets - Raw data as a collection of similarily structured objects.
  • Material and Methods - Descriptions of the computational pipeline.
  • Learning Tasks - Learning tasks defined on raw data.
  • Challenges - Collections of tasks which have a particular theme.
Between data sets and tasks, the relationship is one-to-many, as a data set can give rise to many different learning tasks. A method can also be applied to several different tasks, giving rise to solutions. On the other hand, a task can have many solutions, but each solution belongs to a certain learning task. These relationships are illustrated in the image.


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