Jointly organised by:



"Alexandru Ioan Cuza" University of Iași


"Ovidius" University of Constanța


Vassar College


Academy of Technical Sciences of Romania


Biomedical Text Mining (BioNLP) applies natural language processing (NLP) techniques to identify and extract information from scientific publications in biology, medicine, and chemistry, in order to discover novel knowledge that can contribute to biomedical research.

The growth of BioNLP over the past fifteen years is due in large part to the availability of web-based publication databases such as PubMed and Web of Science coupled with increasing access to anonymized electronic medical/health records. The large size of the biomedical literature and its rapid growth in recent years make literature search and information access a demanding task. Health-care professionals in the clinical domain face a similar problem of information explosion when dealing with the ever-increasing body of available medical/health records in electronic form. Beyond merely identifying texts relevant to a particular interest, BioNLP applies sophisticated NLP information extraction (IE) technologies (e.g., event extraction or entity-relation extraction) to identify and analyze text segments to produce information about, or even models, of phenomena such as drug or protein interactions, gene relations, temporal relations in clinical records, biological processes, etc. Overall, the application of automatic NLP techniques to unstructured text in scientific literature and medical records enables life scientists to both find and exploit this data without the significant effort of manual searching and researching.

EUROLAN-2017 has engaged several well-known researchers in the fields of BioNLP and NLP to provide a comprehensive overview of language processing models and techniques applicable to the biomedical domain, ranging from an introduction to fundamental NLP technologies to the study of use cases and exploitation of available tools and frameworks that support BioNLP. Each tutorial is accompanied by one or two hands-on sessions, in which participants will use text mining tools to explore and exploit several varieties of biomedical language resources, including cloud-based repositories of scientific publications, annotated biomedical corpora, databases and ontologies of biomedical terms, etc. The topics covered in the tutorials and hands-on sessions include:

  • mining biomedical literature
  • entity identification and normalization
  • conceptual graphs extracted from medical texts
  • annotation of semantic content, with applications in medicine and biology
  • medical search engines
  • deep learning for bioinformatics
  • biomedical question/answering
  • clinical data repositories
  • big data and cloud computing in relation with biomedical textual data
  • clinical relationships
  • medical topic modeling
  • medical language systems
  • clinical text analysis
  • text summarization in the biomedical domain
  • event-based text mining for biology and related fields
  • event extraction in medical texts

Over one entire week, internationally reputed specialists will give tutorials and demos (during mornings), and will conduct practical sessions (in the afternoons). A doctoral and young researchers workshop will offer you the opportunity to also present your work in plenary sessions and to validate it against the School specialists’ opinions.