overview.html

<p class="c15"><span class="c3">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 medical 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.</span></p>
<p class="c15"><span class="c3">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.</span></p>
<p class="c15"><span>Inspired by previous NLP shared tasks like &ldquo;</span>&nbsp;<span class="c43"><a class="c14" href="http://alt.qcri.org/semeval2017/task10/">Semeval-2017 Task 10: ScienceIE</a></span><span>&rdquo; and research lines like&nbsp;</span><a href="https://link.springer.com/chapter/10.1007/978-3-319-69904-2_39"><span class="c1">Teleologies</span></a><span class="c3">, both not specifically focussed on the health area, eHealth-KD proposes modelling 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.</span></p>
<p class="c15"><span>The documents used as corpus have been taken from&nbsp;</span><span class="c43"><a class="c14" href="https://medlineplus.gov/xml.html">MedlinePlus</a>&nbsp;</span><span class="c3">and manually processed to make them fit for the task. Additional details are provided at the end of this document.</span></p>
<p class="c24"><span class="c3">To achieve this purpose, three subtasks are presented:</span></p>
<address id="h.1fob9te" class="c41"><span class="c28 c7"><a href="https://tass18-task3.github.io/website/taskA.html">Subtask A: Identification of key phrases</a></span>&nbsp;</address><address id="h.2et92p0" class="c72"><span class="c28 c7"><a href="https://tass18-task3.github.io/website/taskB.html">Subtask B: Classification of key phrases</a></span>&nbsp;</address><address id="h.3dy6vkm" class="c41"><a href="https://tass18-task3.github.io/website/taskC.html"><span class="c28 c7">Subtask C: Setting semantic relationships</span></a></address>