SAEL-RMR

A Synergetic Approach to Extraction, Learning and Reasoning for Machine Reading

Aims

The automated discovery of meaningful knowledge in free text is a current research topic in the text mining and natural language processing fields. It is often referred to as “Machine reading”. The aim of the fundamental research project SAEL-RMR is to find a synergy between information extraction from text, reasoning and machine learning. The application domain is the health domain.

The tasks of LIIR regard advanced information extraction techniques that combine extraction and inferencing.

Partners

LIIR collaborates with Prof. Jesse Davis pf the Declarative Languages and Artificial Intelligence group of KU Leuven, who coordinates the project, and with Prof. Martine De Cock of the Computational Web Intelligence group of Ghent University and of the Center for Data Science of the University of Washington Tacoma, USA and Prof. Steven Schockaert of the School of Computer Science & Informatics of Cardiff University, UK.

Results

The research focuses on entity-relation recognition in biomedical texts. We have explored semi-supervised machine learning techniques, structured learning techniques and the integration of latent factors in the machine learning models. We obtained a second position in the BioNLP 2013 shared task of gene regulation network recognition. We have a regular long paper accepted at the highly competitive BIOSTEC 2015 conference (the 8th International Joint Conference on Biomedical Engineering Systems and Technologies - 6th International Conference on Bioinformatics Models, Methods and Algorithms).



Period From 2012-01-01 to 2017-12-31.
Financed by Research Foundation Flanders (FWO) (G.0356.12)
Supervised by Marie-Francine Moens
Staff Parisa Kordjamshidi
Thomas Provoost
Huaiyu Wan
Contact Marie-Francine Moens

Publications

  1. PROVOOST, Thomas & MOENS, Marie-Francine Detecting Relations in the Gene Regulation Network. In Proceedings of BioNLP 2013. ACL. 2013
  2. PROVOOST, Thomas & MOENS, Marie-Francine Semi-supervised Learning for the BioNLP Gene Regulation Network. BMC Bioinformatics,16 (Suppl 10): S4. 2015
  3. MASSA, Wouter, KORDJAMSHIDI, Parisa, PROVOOST, Thomas and MOENS, Marie-Francine Machine Reading of Biological Texts: Bacteria-Biotope Extraction. In Proceedings of the 8th International Joint Conference on Biomedical Engineering Systems and Technologies - 6th International Conference on Bioinformatics Models, Methods and Algorithms. (nominated for best student paper award) 2015
  4. KORDJAMSHIDI, Parisa, ROTH, Dan & MOENS, Marie-Francine Structured Learning for Spatial Information Extraction from Biomedical Text: Bacteria Biotopes. BMC Bioinformatics, 16: 129. 2015
  5. KORDJAMSHIDI, Parisa, MASSA, Wouter, PROVOOST, Thomas & MOENS, Marie-Francine Machine Reading for Extraction of Bacteria and Habitat Taxonomies. In Proceedings of BIOSTEC extended papers (Lecture Notes in Computer Science). Springer. 2015
  6. WAN, Huaiyu, MOENS, Marie-Francine, LUYTEN, Walter, ZHOU, Xuezhong, MEI, Qiaozhu, LIU, Lu & TANG, Jie Extracting Relations From Traditional Chinese Medicine Literature via Heterogeneous Entity Networks. Journal of the American Medical Informatics Association(JAMIA) (in press). 2015


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