The present work aims to create a shallow parser for Modern Greek subject/object detection, using machine learning techniques. The parser relies on limited resources. Experiments with equivalent input and the same learning techniques were conducted for English, as well, proving that the methodology can be adjusted to deal with other languages with only minor modifications. For the first time, the class imbalance problem concerning Modern Greek syntactically annotated data is successfully addressed. © 2011 IFIP International Federation for Information Processing.
CITATION STYLE
Karozou, A., & Kermanidis, K. L. (2011). Multiple Criteria Performance Analysis of Non- dominated Sets Obtained by Multi-objective Evolutionary Algorithms. IFIP Advances in Information and Communication Technology, 364(PART 2), 190–195. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-80055044795&partnerID=tZOtx3y1
Mendeley helps you to discover research relevant for your work.