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The Center for Computational Linguistics

Bucharest Cities Computational Linguistics Information retrieval Natural Language Processing Research domains

The Center of Computational Linguistics, lead by Prof. Liviu Dinu, Ph.D. and Prof. Emil Ionescu, Ph.D., is a multi-disciplinary research area that addresses the automatic processing of human language for a range of tasks. We are a high-level research center inside the University of Bucharest, with our members having gained experience (as graduates, PhD, master students, or full employees) from the Faculty of Letters, the Faculty of Foreign Languages and Literatures, and the Faculty of Mathematics and Computer Science.

 
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IDS – Intelligent Distributed Systems Research Group

Agents and Multi-agent Systems Craiova Data Mining Social Network Analysis Web mining

The Intelligent Distributed Systems Research Group at the University of Craiova is lead by Prof. Costin Badica, PhD. The group's research interest covers a hybrid palette of methods and techniques derived from classical artificial intelligence, computational intelligence, and intelligent agents. Distributed systems contain loosely-coupled components running on networked computers that communicate and coordinate their actions by message transfer. The new generation of intelligent distributed systems is expected to pose special challenges of adaptation and fruitful combination of results of both areas.

 
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Knowledge Engineering Group

Classification Cluj-Napoca Data Mining Information retrieval Knowledge Representation and Reasoning Machine Learning Medical data mining Natural Language Processing Social Network Analysis Text mining

The Knowledge Engineering Group (KEG), lead by Prof. Rodica Potolea, PhD, is addressing problems for knowledge extraction, representation, storage and management that the information era has brought in various segments of human activity due to data overload.

The main fundamental theoretical aspects our group focuses on are: dealing with problem-specific features extraction from both structured data, pre-processing techniques for handling noisy and/or incomplete data, learning from balanced/unbalanced and structured/unstructured data.

 
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