ESSENCE: a portable methodology for building information extraction systems

One of the most important issues when constructing an Information Extraction System is how to obtain the knowledge needed for identifying relevant information in a document. A manual approach not only is an expensive solution but also has a negative effect on the portability of the system across domains. To automatize the knowledge acquisition process may partially solve this problem even if a human expert takes part in it only for specific tasks. This work presents a methodology ({sc Essence}) to automatically learn information extraction patterns from unrestricted text corpus representative of the domain. The methodology includes different steps from which we stress the specific pattern generalization process. Generalization reduces the pattern base and therefore reduces the amount of information to validate by an expert. As we will see, the use of the lexical knowledge along with the lexico-semantic relations from WordNet are our basis knowledge source, especially, for the generalization process.Postprint (published version

Feature selection for support vector machines by alignment with ideal kernel

Feature selection has several potentially beneficial uses in machine learning. Some of them are to improve the performance of the learning method by removing noisy features, to reduce the feature set in data collection, and to better understand the data. In this report we present how to use empirical alignment, a well known measure for the fitness of kernels to data labels, to perform feature selection for support vector machines. We show that this measure improves the results obtained with other widely used measures for feature selection (like information gain or correlation) in linearly separable problems. We also show how alignment can be successfully used to select relevant features in non-linearly separable problems when using support vector machines.Postprint (published version

The polysemy of the words that children learn over time

Here we study polysemy as a potential learning bias in vocabulary learning in children. We employ a massive set of transcriptions of conversations between children and adults in English, to analyze the evolution of mean polysemy in the words produced by children whose ages range between 10 and 60 months. Our results show that mean polysemy in children increases over time in two phases, i.e. a fast growth till the 31st month followed by a slower tendency towards adult speech. In contrast, no dependency with time is found in adults. This may suggest that children have a preference for non-polysemous words in their early stages of vocabulary acquisition. Our hypothesis is twofold: (a) polysemy is a standalone bias or (b) polysemy is a side-effect of other biases. Interestingly, the bias for low polysemy above weakens when controlling by syntactic category (noun, verb, adjective or adverb). The pattern of the evolution of polysemy suggests that both hypotheses may apply to some extent, and that (b) would originate from a combination of the well-known preference for nouns and the lower polysemy of nouns with respect to other syntactic categories.Peer ReviewedPostprint (author's final draft

Kernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia

The current wide access to data from different neuroimaging techniques has permitted to obtain data to explore the possibility of finding objective criteria that can be used for diagnostic purposes. In order to decide which features of the data are relevant for the diagnostic task, we present in this paper a simple method for feature selection based on kernel alignment with the ideal kernel in support vector machines (SVM). The method presented shows state-of-the-art performance while being more efficient than other methods for feature selection in SVM. It is also less prone to overfitting due to the properties of the alignment measure. All these abilities are essential in neuroimaging study, where the number of features representing recordings is usually very large compared with the number of recordings. The method has been applied to a dataset in order to determine objective criteria for the diagnosis of schizophrenia. The dataset analyzed has been obtained from multichannel magnetoencephalogram (MEG) recordings, corresponding to the recordings during the performance of a mismatch negativity (MMN) auditory task by a set of schizophrenia patients and a control group. All signal frequency bands are analyzed (from d (1–4 Hz) to high frequency ¿ (60–200 Hz)) and the signal correlations among the different sensors for these frequencies are used as features.Peer ReviewedPostprint (author's final draft

Guía de actividades docentes para la formación en integración e igualdad de oportunidades por razón de discapacidad en las enseñanzas técnicas: accesibilidad universal y diseño para todos

Esta guía se ha promovido desde la Universidad Politécnica de Cataluña (UPC) en un momento caracterizado por la transición a los nuevos grados y másteres. Elaborada con un enfoque práctico y didáctico, quiere ser una herramienta de fácil uso y lectura para el profesorado de las carreras técnicas de cualquier universidad española, aportando ejemplos de aplicación de los principios de diseño para todos y criterios de accesibilidad universal en su práctica docente

Guía de actividades docentes para la formación en integración e igualdad de oportunidades por razón de discapacidad en las enseñanzas técnicas: accesibilidad universal y diseño para todos

Estudio financiado por el MEC, Programa Estudio y AnálisisPostprint (published version

PROGRAMACIÓ METÒDICA (Examen 2n quadrimestre)

Examen temes 1 i 2: Especificació i correctesa. Recursivitat, amb solucionsResolve