A framework to operate transformations between distinct models differing in modelling concepts

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

La notion d’Espace dans la création musicale : idées, concepts et attributions Une réflexion à propos d’espaces intentionnellement perçus ou composés de l’entité sonore

Notre étude présente une réflexion à propos de espaces musicaux composables. Cette réflexion inclue l’usage intentionnel des qualités sonores perçues dans la construction de sons et donc dans la composition musicale. Nos recherches se trouvent à l’intersection entre le son, comme phénomène physique qui se propage dans un espace, les sensations spatiales qui peuvent être engendrées par la perception auditive de ses certaines caractéristiques, et la pratique de la composition musicale d’espaces de sons dans les œuvres. Nous développons l’idée d’une entité sonore composable dès sa microstructure. Nous la concevons par analogie avec les objets du monde visible et palpable. Nous utilisons les idées de volume, forme et matière ainsi que celles de position et de mouvement des objets matériels, comme métaphore pour concevoir des entités sonores complexes et des espaces musicaux composés dans lesquels elles seront intégrées. Cette entité sonore constituée d’un ensemble d’éléments disparates, permet le développement de réseaux opératoires manipulables dans, la pratique, par les compositeurs.

Automatic extraction of concepts from texts and applications

The extraction of relevant terms from texts is an extensively researched task in Text- Mining. Relevant terms have been applied in areas such as Information Retrieval or document clustering and classification. However, relevance has a rather fuzzy nature since the classification of some terms as relevant or not relevant is not consensual. For instance, while words such as "president" and "republic" are generally considered relevant by human evaluators, and words like "the" and "or" are not, terms such as "read" and "finish" gather no consensus about their semantic and informativeness. Concepts, on the other hand, have a less fuzzy nature. Therefore, instead of deciding on the relevance of a term during the extraction phase, as most extractors do, I propose to first extract, from texts, what I have called generic concepts (all concepts) and postpone the decision about relevance for downstream applications, accordingly to their needs. For instance, a keyword extractor may assume that the most relevant keywords are the most frequent concepts on the documents. Moreover, most statistical extractors are incapable of extracting single-word and multi-word expressions using the same methodology. These factors led to the development of the ConceptExtractor, a statistical and language-independent methodology which is explained in Part I of this thesis. In Part II, I will show that the automatic extraction of concepts has great applicability. For instance, for the extraction of keywords from documents, using the Tf-Idf metric only on concepts yields better results than using Tf-Idf without concepts, specially for multi-words. In addition, since concepts can be semantically related to other concepts, this allows us to build implicit document descriptors. These applications led to published work. Finally, I will present some work that, although not published yet, is briefly discussed in this document.