Organizers

Sophia Ananiadou (Salford University, Manchester)
Jong C. Park (KAIST, Daejeon)

With biomedical literature expanding so rapidly, there is an urgent need to discover and organise knowledge extracted from texts. Although factual databases contain crucial information the overwhelming amount of new knowledge remains in textual form (e.g. MEDLINE). In addition, new terms are constantly coined as the relationships linking new genes, drugs, proteins etc. As the size of biomedical literature is expanding, more systems are applying a variety of methods to automate the process of knowledge acquisition and management. These include a variety of techniques such as statistics, machine learning, SVMs, deep or shallow linguistic or domain knowledge etc. Some NLP related topics are challenging in biomedicine such as: dynamic terminology management, named-entity recognition, integration with non-textual resources, discovery of named relationships, populating and updating existing ontologies / taxonomies. The aim of this thematic session is to examine issues and challenges in the area of biomedical text mining.

Submission

The submission procedures and deadline for thematic sessions are the same as those for the main conference