BioASQ organizes challenges on biomedical semantic indexing and question answering (QA). The challenges include tasks relevant to hierarchical text classification, machine learning, information retrieval, QA from texts and structured data, multi-document summarization and many other areas.

Monetary and other prizes are awarded to the best performing systems.

 

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The Challenge

The BioASQ challenge comprises the following three tasks.

BioASQ Task on Large-Scale Online Biomedical Semantic Indexing

In this task, the participants are asked to classify new PubMed documents, before PubMed curators annotate (in effect, classify) them manually. The classes come from the MeSH hierarchy. As new manual annotations become available, they are used to evaluate the classification performance of participating systems.

BioASQ Task on Biomedical Semantic QA (involves IR, QA, summarization and more)

This task uses benchmark datasets containing development and test questions, in English, along with gold standard (reference) answers constructed by a team of biomedical experts. The participants have to respond with relevant concepts, articles, snippets and RDF triples, from designated resources, as well as exact and 'ideal' answers.

BioASQ Task on Funding Information Extraction From Biomedical Literature

In this task, the participants are asked to extract grant ids and grant agencies from the full text of PumMed documents, available in PubMed Central. Annotations from PubMed are used to evaluate the information extraction performance of participating systems

 

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