EUROLAN-2017 – Summer School on Biomedical Text Processing

Call for Papers May 27, 2017 No Comments

EUROLAN-2017 – Summer School on Biomedical Text Processing
10 – 17 September 2017, Constanța, Romania

The 13th in the series of EUROLAN Schools

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 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. Overall, the application of automatic NLP techniques to unstructured text in scientific literature and medical records enables life scientists to find and exploit this data.

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. Tutorial are accompanied by hands-on sessions.

Invited Lecturers and Topics (T=tutorial; H = Hands-on session)

Lecturer: Mihaela Breabăn – “Alexandru Ioan Cuza” University of Iași (Romania)
T: Open-Source Frameworks for Big Data Processing
H: Textual data processing on Hadoop

Lecturer: Kevin Cohen – University of Colorado School of Medicine (USA) and LIMSI, CNRS, Université Paris-Saclay, Orsay (France)
T: Language and linguistics in NLP/NLP for biomedical language
H: Empirical investigations of the implications of the nature of biomedical language for the design of experiments in natural language processing

Lecturer: Noa Patricia Cruz Diaz – Virgen del Rocio University Hospital (Spain)
T: Negation and Speculation Detection in Biomedical Texts
H: Rule-based versus machine-learning tools for automatic identification of negation

Lecturer: Eric Gaussier – University Grenoble Alps (France)
T: Information extraction. Techniques for Mining Biomedical Texts
H: Analysis and discussion on (some) information extraction tools for biomedical texts (together with Pierre Zweigenbaum)

Lecturer: Nancy Ide – Vassar College (USA)
T: Mining Scientific Literature with the LAPPS Grid
H: Data discovery and mining using major scientific publication databases

Lecturer: Pierre Zweigenbaum – LIMSI, CNRS, Université Paris-Saclay, Orsay (France)
T: Detecting Medical Concepts in Clinical Texts (named entity extraction and use of specialized vocabularies, terminologies, ontologies)


EUROLAN-2017 is hosted by the “Ovidius” University of Constanța, Faculty of Mathematics and Computer Science and Faculty of Medicine in Constanța, Romania.

Satellite event

MEDA-2017 – workshop on Curative Power of MEdical DAta will take place on September 14; see details at


Low-cost accommodation for EUROLAN students is available in the University’s hostel (shared double rooms). Alternatively, participants may opt for a number of hotels in the city of Constanța or Mamaia.

Registration and fee

Before 4 August:  400 EUR
5 August and later:  450 EUR

Fees applicable only to students; for other types of participants, see

Important Dates

•       Registration open: May 31, 2017
•       Last day for early registration: August 4, 2017
•       Last day for late registration: August 31, 2017
•       EUROLAN School: September 10-17, 2017

Program Committee and Contacts

Dan Cristea <>
Nancy Ide <>
Dan Tufiș <>


•       Romanian Academy
•       “Alexandru Ioan Cuza” University in Iași
•       “Ovidius” University in Constanța
•       Vassar College
•       Technical Sciences Academy of Romania

Achraf Othman

Dr. Achraf is a senior research specialist in Accessibility and Assistive Technology for People with disabilities and Machine Translation and Machine Learning.