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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

  • 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)
  • Task CEM task BioCreative IV 2015-07-14 11:21
    named entity recognition of chemical compound mentions
  • Data CHEMDNER task of BioCreative IV 2015-07-14 11:16
    CHEMDNER Task: Chemical compound and drug name recognition task

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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