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Emotions are complex human phenomena. Numberless researchers have attempted a variety of approaches to model these phenomena and to find the optimal set of emotion descriptors. In this paper, we search for the most appropriate acoustic features to estimate the emotional content in speech based on a continuous multi-dimensional model of emotions. We analyze a set of 6,920 features using the feature selection method known as Linear Forward Selection. We studied the importance of the features dividing them into groups and working with two databases, one in English and one in German to analyze the multi-lingual importance of features and to know if these features are important regardless of the language.

This paper examines the importance of different groups of speech acoustic features in the estimation of emotional primitives which define a three-dimensional continuous model of emotions. A set of proposed features is extracted from a database of German spontaneous emotional speech. This features set tries to represent several aspects of emotional content in speech that have been discussed separately in other works. Features selection and dimensionality reduction techniques are applied to find the features subsets that best estimate the emotional primitives Valence, Activation and Dominance. Finally, the emotional primitives are estimated using different classifiers based on the proposed features getting better results than the ones previously reported on related works.

Multi-document summarization systems must be able to draw the "best" information from a set of documents.In this paper we propose a novel extractive approach for multidocument summarization based on the detection of locally relevant sentences. Our main hypothesis is that by extracting relevant sentences from each document within a collection, instead of considering all documents at once, the final multi-document summary will be of higher quality. Performed experiments showed that the proposed method is able to outperform conventional baselines as well as traditional approaches by constructing summaries of high quality according to the ROUGE evaluation metrics.

Finding accurate information on the Web has become a challenge due to the increment in the number of documents available online. Current search engines retrieve relevant documents to general - often short - user queries, but fail extracting answers to simple factual questions in natural language. This work presents the basis of a statistical question answering system capable to find answers to factual questions in Spanish language from the Web. This approach is supported on data redundancy rather than on sophisticated linguistic analyses of either questions and candidate answers. Preliminary results show that it is feasible to find concise and accurate answers from the Web to factual questions made in Spanish language. The study also concludes that the available Spanish documents in the Web are redundant enough in order to apply statistical methods like those described in this document in order to provide better mechanisms for information access.