Constellations and the unsupervised learning of graphs

In this paper, we propose a novel method for the unsupervised clustering of graphs in the context of the constellation approach to object recognition. Such method is an EM central clustering algorithm which builds prototypical graphs on the basis of fast matching with graph transformations. Our experiments, both with random graphs and in realistic situations (visual localization), show that our prototypes improve the set median graphs and also the prototypes derived from our previous incremental method. We also discuss how the method scales with a growing number of images.

Una nueva técnica de construcción de grafos semánticos para la desambiguación bilingüe del sentido de las palabras

En este trabajo presentamos unos resultados preliminares obtenidos mediante la aplicación de una nueva técnica de construcción de grafos semánticos a la tarea de desambiguación del sentido de las palabras en un entorno multilingüe. Gracias al uso de esta técnica no supervisada, inducimos los sentidos asociados a las traducciones de la palabra ambigua considerada en la lengua destino. Utilizamos las traducciones de las palabras del contexto de la palabra ambigua en la lengua origen para seleccionar el sentido más probable de la traducción. El sistema ha sido evaluado sobre la colección de datos de una tarea de desambiguación multilingüe que se propuso en la competición SemEval-2010, consiguiendo superar los resultados de todos los sistemas no supervisados que participaron en aquella tarea.

Application of Text Summarization techniques to the Geographical Information Retrieval task

Automatic Text Summarization has been shown to be useful for Natural Language Processing tasks such as Question Answering or Text Classification and other related fields of computer science such as Information Retrieval. Since Geographical Information Retrieval can be considered as an extension of the Information Retrieval field, the generation of summaries could be integrated into these systems by acting as an intermediate stage, with the purpose of reducing the document length. In this manner, the access time for information searching will be improved, while at the same time relevant documents will be also retrieved. Therefore, in this paper we propose the generation of two types of summaries (generic and geographical) applying several compression rates in order to evaluate their effectiveness in the Geographical Information Retrieval task. The evaluation has been carried out using GeoCLEF as evaluation framework and following an Information Retrieval perspective without considering the geo-reranking phase commonly used in these systems. Although single-document summarization has not performed well in general, the slight improvements obtained for some types of the proposed summaries, particularly for those based on geographical information, made us believe that the integration of Text Summarization with Geographical Information Retrieval may be beneficial, and consequently, the experimental set-up developed in this research work serves as a basis for further investigations in this field.