PILOH – Development of a software tool for solving linguistic engineering problems by heuristic optimization.

Principal investigator: Abraham Duarte Funding entities: URJC y Comunidad de Madrid (URJC-CM-2006-CET-0603) Duration: 01/01/2007 – 31/12/2007


The processing and organization of the enormous amount of information in electronic format that is currently available has become a necessity in the Information Society in which we live. Consequently, it would not make sense to have large repositories of linguistic information from which we could not extract useful knowledge. This project is framed within the area of heuristic optimization applied to problems of Natural Language Processing and Linguistic Engineering. In this project we will develop software for the optimization of different optimization problems with the aim of solving two of the major problems currently facing the field of Natural Language Processing: automatic classification and clustering of documents.

The problems to be addressed are based on structured models, i.e., where a complete mathematical description or formulation is known. Different efficient resolution models based on metaheuristic procedures will be proposed. The methods proposed will be compared with the best existing resolution methods for such problems, both in the academic and commercial domains. It is intended that this will result in both an application that provides high quality solutions and scientific publications of international impact.

Isaac Lozano-Osorio
Isaac Lozano-Osorio
Artificial Intelligence Phd Student

Isaac Lozano graduated with a double degree in Computer Engineering and Computer Engineering from the Universidad Rey Juan Carlos, where he was awarded the prize for the Best Final Project. Subsequently, he completed a Master in Artificial Intelligence Research (UIMP). His main research interests are focused on the interface between Computer Science, Artificial Intelligence and Operations Research. Most of his publications deal with the development of metaheuristic procedures for graph modeled optimization problems.