896 resultados para Opinion discourse
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This article discusses the construction of tri-sector partnerships in three projects conducted in Brazil in different fields of intervention of public policy (access to water, basic education and performance of boards of rights of children and adolescents). Collaborative articulations involving the players from three sectors (the State, civil society and the market) are practices that are little studied in the Brazilian and even in the international context, as tri-sector partnerships are rare, despite the proliferation of lines of discourse in support of alliances between governments and civil society or between companies and NGOs in the management of public policy. As a research strategy, this study resorted to cooperative inquiry, a method that involves breaking down the boundaries between the subjects and the objects of the analysis. Besides working toward a better understanding of the challenges of building tri-sector partnerships in the Brazilian context, the article also tries to show the relevance to public policy studies of investigative methods based on the subjects studied, as a means of developing an understanding of the practices, lines of discourse and dilemmas linked to social action in social programs.
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Collection : Les archives de la Révolution française ; 11.1a.128
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Collection : Les archives de la Révolution française ; 11.1a.446
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Abstract: To cluster textual sequence types (discourse types/modes) in French texts, K-means algorithm with high-dimensional embeddings and fuzzy clustering algorithm were applied on clauses whose POS (part-ofspeech) n-gram profiles were previously extracted. Uni-, bi- and trigrams were used on four 19th century French short stories by Maupassant. For high-dimensional embeddings, power transformations on the chi-squared distances between clauses were explored. Preliminary results show that highdimensional embeddings improve the quality of clustering, contrasting the use of bi and trigrams whose performance is disappointing, possibly because of feature space sparsity.
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In this article, we present the current state of our work on a linguistically-motivated model for automatic summarization of medical articles in Spanish. The model takes into account the results of an empirical study which reveals that, on the one hand, domain-specific summarization criteria can often be derived from the summaries of domain specialists, and, on the other hand, adequate summarization strategies must be multidimensional, i.e., cover various types of linguistic clues. We take into account the textual, lexical, discursive, syntactic and communicative dimensions. This is novel in the field of summarization. The experiments carried out so far indicate that our model is suitable to provide high quality summarizations.