Using WordNet relations and semantic classes in information retrieval tasks

In this paper we explore the use of semantic classes in an existing information retrieval system in order to improve its results. Thus, we use two different ontologies of semantic classes (WordNet domain and Basic Level Concepts) in order to re-rank the retrieved documents and obtain better recall and precision. Finally, we implement a new method for weighting the expanded terms taking into account the weights of the original query terms and their relations in WordNet with respect to the new ones (which have demonstrated to improve the results). The evaluation of these approaches was carried out in the CLEF Robust-WSD Task, obtaining an improvement of 1.8% in GMAP for the semantic classes approach and 10% in MAP employing the WordNet term weighting approach.

Technology diffusion and its effects on social inequalities

We develop a dynamic general-equilibrium framework in which growth is driven by skill-biased technology diffusion. The model incorporates leisure–labor decisions and human capital accumulation through education. We are able to reproduce the trends in income inequality and labor and skills supplies observed in the United States between 1969 and 1996. The paper also provides an explanation for why more individuals invest in human capital when the investment premium is going down, and why the skill-premium goes up when the skills supply is increasing.