The development of a knowledge-based society - Challenges for e-learning and adult students in higher education

This chapter appears in Encyclopaedia of Distance Learning 2nd Edition edit by Rogers, P.; Berg, Gary; Boettecher, Judith V.; Howard, Caroline; Justice, Lorraine; Schenk, Karen D.. Copyright 2009, IGI Global, www.igi-global.com. Posted by permission of the publisher. URL: http://www.igi-global.com/reference/ details.asp?ID=9703&v=tableOfContents

Synthesis, characterization and applications of new schiff base fluorescent chemosensors for metal and DNA interactions: conventional and "green" approaches

Dissertação apresentada para a obtenção do Grau de Doutor em Química Sustentável, especialidade de Química-Física Inorgânica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Characterization of processed materials by electrical currents: development of equipment and applications

Dissertação para obtenção do Grau de Mestre em Engenharia Mecânica

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.

Exploring Literary Landscapes: From Texts to Spatiotemporal Analysis through Collaborative Work and GIS

This article argues that the study of literary representations of landscapes can be aided and enriched by the application of digital geographic technologies. As an example, the article focuses on the methods and preliminary findings of LITESCAPE.PT—Atlas of Literary Landscapes of Mainland Portugal, an on-going project that aims to study literary representations of mainland Portugal and to explore their connections with social and environmental realities both in the past and in the present. LITESCAPE.PT integrates traditional reading practices and ‘distant reading’ approaches, along with collaborative work, relational databases, and geographic information systems (GIS) in order to classify and analyse excerpts from 350 works of Portuguese literature according to a set of ecological, socioeconomic, temporal and cultural themes. As we argue herein this combination of qualitative and quantitative methods—itself a response to the difficulty of obtaining external funding—can lead to (a) increased productivity, (b) the pursuit of new research goals, and (c) the creation of new knowledge about natural and cultural history. As proof of concept, the article presents two initial outcomes of the LITESCAPE.PT project: a case study documenting the evolving literary geography of Lisbon and a case study exploring the representation of wolves in Portuguese literature.