eHealth KD @ IberLEF 2020

Organized by tass18-task3 - Current server time: July 9, 2020, 8:52 a.m. UTC

First phase

Development
Feb. 24, 2020, midnight UTC

End

Competition Ends
May 5, 2020, 11:59 p.m. UTC

Natural Language Processing (NLP) methods are increasingly being used to mine knowledge from unstructured health texts. Recent advances in health text processing techniques are encouraging researchers and health domain experts to go beyond just reading the information included in published texts (e.g. academic manuscripts, clinical reports, etc.) and structured questionnaires, to discover new knowledge by mining health contents. This has allowed other perspectives to surface that were not previously available.

Over the years many eHealth challenges have taken place, which have attempted to identify, classify, extract and link knowledge, such as Semevals, CLEF campaigns and others.

The eHealth-KD 2020 proposes –as the previous editions eHealth-KD 2019 and eHealth-KD 2018– modeling the human language in a scenario in which Spanish electronic health documents could be machine readable from a semantic point of view. With this task, we expect to encourage the development of software technologies to automatically extract a large variety of knowledge from eHealth documents written in the Spanish Language.

Even though this challenge is oriented to the health domain, the structure of the knowledge to be extracted is general-purpose. The semantic structure proposed models four types of information units. Each one represents a specific semantic interpretation, and they make use of thirteen semantic relations among them. The following sections provide a detailed presentation of each unit and relation type. An example is provided in the following picture.

This challenge can be of interest for experts in the field of natural language processing, specifically for those working on automatic knowledge extraction and discovery. It is not a requirement to have expertise in health texts processing for dealing with the eHealth-KD task, due to the general purpose of the semantic schema defined. Nevertheless, eHealth researchers could find interesting this challenge to evaluate their technologies that rely on health domain knowledge.

Description of the Subtasks

To simplify the evaluation process, two subtasks are presented:

  1. Entity recognition
  2. Relation extraction

For more details browse this link.

There are four evaluation scenarios:

  1. A main scenario covering both tasks
  2. An optional scenario evaluating subtask A
  3. An optional scenario evaluating subtask B
  4. An optional scenario in a completely different domain [for Transfer Learning purpose]

The datasets can be accessed through this link.

 

Evaluation Results

The avaluation results can be found at the following link.

By submitting results to this competition, you consent to the public release of your scores at the IberLLEF 2020 workshop and in the associated proceedings, at the task organizers' discretion. Scores may include, but are not limited to, automatic and manual quantitative judgements, qualitative judgements, and such other metrics as the task organizers see fit. You accept that the ultimate decision of metric choice and score value is that of the task organizers.

You further agree that the task organizers are under no obligation to release scores and that scores may be withheld if it is the task organizers' judgement that the submission was incomplete, erroneous, deceptive, or violated the letter or spirit of the competition's rules. Inclusion of a submission's scores is not an endorsement of a team or individual's submission, system, or science.

You further agree that your system may be named according to the team name provided at the time of submission, or to a suitable shorthand as determined by the task organizers.

You agree not to redistribute the test data except in the manner prescribed by its licence.

Development

Start: Feb. 24, 2020, midnight

Testing

Start: April 20, 2020, midnight

Competition Ends

May 5, 2020, 11:59 p.m.

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