Report of MIRACLE team for the Ad-Hoc track in CLEF 2006

This paper presents the 2006 MIRACLE’s team approach to the AdHoc Information Retrieval track. The experiments for this campaign keep on testing our IR approach. First, a baseline set of runs is obtained, including standard components: stemming, transforming, filtering, entities detection and extracting, and others. Then, a extended set of runs is obtained using several types of combinations of these baseline runs. The improvements introduced for this campaign have been a few ones: we have used an entity recognition and indexing prototype tool into our tokenizing scheme, and we have run more combining experiments for the robust multilingual case than in previous campaigns. However, no significative improvements have been achieved. For the this campaign, runs were submitted for the following languages and tracks: - Monolingual: Bulgarian, French, Hungarian, and Portuguese. - Bilingual: English to Bulgarian, French, Hungarian, and Portuguese; Spanish to French and Portuguese; and French to Portuguese. - Robust monolingual: German, English, Spanish, French, Italian, and Dutch. - Robust bilingual: English to German, Italian to Spanish, and French to Dutch. - Robust multilingual: English to robust monolingual languages. We still need to work harder to improve some aspects of our processing scheme, being the most important, to our knowledge, the entities recognition and normalization

Miracle’s 2005 Approach to Cross-lingual Information Retrieval

This paper presents the 2005 Miracle’s team approach to Bilingual and Multilingual Information Retrieval. In the multilingual track, we have concentrated our work on the merging process of the results of monolingual runs to get the multilingual overall result, relying on available translations. In the bilingual and multilingual tracks, we have used available translation resources, and in some cases we have using a combining approach

Miracle’s 2005 Approach to Monolingual Information Retrieval

This paper presents the 2005 Miracle’s team approach to Monolingual Information Retrieval. The goal for the experiments in this year was twofold: continue testing the effect of combination approaches on information retrieval tasks, and improving our basic processing and indexing tools, adapting them to new languages with strange encoding schemes. The starting point was a set of basic components: stemming, transforming, filtering, proper nouns extracting, paragraph extracting, and pseudo-relevance feedback. Some of these basic components were used in different combinations and order of application for document indexing and for query processing. Second order combinations were also tested, by averaging or selective combination of the documents retrieved by different approaches for a particular query

DAEDALUS at PAN 2014: Guessing tweet author's gender and age

This paper describes our participation at PAN 2014 author profiling task. Our idea was to define, develop and evaluate a simple machine learning classifier able to guess the gender and the age of a given user based on his/her texts, which could become part of the solution portfolio of the company. We were interested in finding not the best possible classifier that achieves the highest accuracy, but to find the optimum balance between performance and throughput using the most simple strategy and less dependent of external systems. Results show that our software using Naive Bayes Multinomial with a term vector model representation of the text is ranked quite well among the rest of participants in terms of accuracy

Experiencias con la tecnología BIM en la realización de trabajos de fin de grado en ingenierías de la rama industrial

Objetivo: El presente artículo trata de exponer las experiencias del uso de la tecnología BIM en los Trabajos de Fin de Grado (TFG) en la Escuela Politécnica Superior de la Universidad de Sevilla. Diseño / metodología / enfoque: La implementación de la tecnología BIM en la realización de TFG en ingenierías de la rama industrial ha sido una experiencia enriquecedora tanto para los directores de los trabajos como para los alumnos. Por ello se pretende explicar cómo se han desarrollado los primeros TFG en modelado BIM. Resultados: Los principales resultados obtenidos que podemos destacar son: la motivación del alumno en el uso de esta herramienta tecnológica, incentivar la ingeniería colaborativa y la incorporación de la realidad virtual en la edificación industrial. Originalidad : Se van a presentar los primeros TFG realizados por los alumnos María Nuria Ortega López y Pablo Cabrera Aguilar como ejemplos de éxito de la aplicación del modelado BIM en la ingeniería. El primer proyecto es la elaboración y diseño de un concesionario de vehículos, donde se realiza una comparativa desde la implementación del modelado con dos softwares BIM diferentes. El segundo proyecto realiza el cálculo estructural y el diseño y modelado de un hangar para un A400M.Purpose: This paper tries to expose the experiences of use of the BIM technology in the End of Degree Projects (EDP) in the Superior Polytechnic School of the University of Seville. Design/methodology/approach: The implementation of BIM technology in the realization of EDP in engineering of the industrial branch has been an enriching experience for both the directors of the work and the students. Therefore, it is intended to explain how the first EDP were developed in BIM modeling. Findings: The main results that we can highlight are: the student's motivation in the use of this technological tool, encourage collaborative engineering, and the incorporation of virtual reality in industrial building. Originality: The first final degree projects carried out by the students María Nuria Ortega López and Pablo Cabrera Aguilar will be presented as successful examples of the application of BIM modeling in engineering. The first project is the development and design of a vehicle where a comparison is made from the implementation of the modeling with two different BIM softwares. The second project performs the structural calculation and the design and modeling of a hangar for an A400M

MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation

This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2007. Our areas of expertise do not include image analysis, thus we approach this task as a machine-learning problem, regardless of the domain. FIRE is used as a black-box algorithm to extract different groups of image features that are later used for training different classifiers in order to predict the IRMA code. Three types of classifiers are built. The first type is a single classifier that predicts the complete IRMA code. The second type is a two level classifier composed of four classifiers that individually predict each axis of the IRMA code. The third type is similar to the second one but predicts a combined pair of axes. The main idea behind the definition of our experiments is to evaluate whether an axis-by-axis prediction is better than a prediction by pairs of axes or the complete code, or vice versa. We submitted 30 experiments to be evaluated and results are disappointing compared to other groups. However, the main conclusion that can be drawn from the experiments is that, irrespective of the selected image features, the axis-by-axis prediction achieves more accurate results not only than the prediction of a combined pair of axes but also, in turn, than the prediction of the complete IRMA code. In addition, data normalization seems to improve the predictions and vector-based features are preferred over histogram-based ones

Report of MIRACLE team for the Ad-Hoc track in CLEF 2007

This paper presents the 2007 MIRACLE’s team approach to the AdHoc Information Retrieval track. The work carried out for this campaign has been reduced to monolingual experiments, in the standard and in the robust tracks. No new approaches have been attempted in this campaign, following the procedures established in our participation in previous campaigns. For this campaign, runs were submitted for the following languages and tracks: - Monolingual: Bulgarian, Hungarian, and Czech. - Robust monolingual: French, English and Portuguese. There is still some room for improvement around multilingual named entities recognition

MIRACLE at ImageCLEFannot 2008: Classification of Image Features for Medical Image Annotation

This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2008. A lot of effort was invested this year to develop our own image analysis system, based on MATLAB, to be used in our experiments. This system extracts a variety of global and local features including histogram, image statistics, Gabor features, fractal dimension, DCT and DWT coefficients, Tamura features and coocurrency matrix statistics. Then a k-Nearest Neighbour algorithm analyzes the extracted image feature vectors to determine the IRMA code associated to a given image. The focus of our experiments is mainly to test and evaluate this system in-depth and to make a comparison among diverse configuration parameters such as number of images for the relevance feedback to use in the classification module

MIRACLE Retrieval Experiments with East Asian Languages

This paper describes the participation of MIRACLE in NTCIR 2005 CLIR task. Although our group has a strong background and long expertise in Computational Linguistics and Information Retrieval applied to European languages and using Latin and Cyrillic alphabets, this was our first attempt on East Asian languages. Our main goal was to study the particularities and distinctive characteristics of Japanese, Chinese and Korean, specially focusing on the similarities and differences with European languages, and carry out research on CLIR tasks which include those languages. The basic idea behind our participation in NTCIR is to test if the same familiar linguisticbased techniques may also applicable to East Asian languages, and study the necessary adaptations

MIRACLE’s Naive Approach to Medical Images Annotation

One of the proposed tasks of the ImageCLEF 2005 campaign has been an Automatic Annotation Task. The objective is to provide the classification of a given set of 1,000 previously unseen medical (radiological) images according to 57 predefined categories covering different medical pathologies. 9,000 classified training images are given which can be used in any way to train a classifier. The Automatic Annotation task uses no textual information, but image-content information only. This paper describes our participation in the automatic annotation task of ImageCLEF 2005