The reception of British literature under dictatorships in Hungary and Portugal

Esta tese tem como objectivo comparar aspectos da recepção da literatura britânica durante o Estado Novo em Portugal, e na Hungria durante a era comunista. Na primeira parte da dissertação fica contextualizada e analisada a alteração da posição da literatura britânica nos dois países, enquanto na segunda parte se comparam facetas da recepção da mesma, contrastando as traduções literárias publicadas em Portugal e na Hungria entre 1949 e 1974, altura em que ambos os países foram dirigidos simultaneamente por regimes autocratas. Este estudo faz-se, em parte, através da comparação de manuais escolares e seletas, a fim de examinar os diferentes métodos usados pelos dois regimes ditatoriais politicamente opostos na tentativa de moldarem os seus leitores. É dada especial atenção à literatura proibida e aos motivos para as atitudes restritivas diferenciadas em relação a certos escritores na era de Salazar e na Hungria durante o período de Bloco de Leste. A tese conclui que a abordagem divergente em relação às obras literárias britânicas, bem como à literatura em geral, é principalmente enraizada na ideologia de governação diametralmente oposta dos dois regimes ditatoriais. A crença idealista no poder educativo da literatura em estabelecer o socialismo na Hungria está em nítido contraste com a política de cultura obscurantista do Estado Novo. Mesmo quando o apoio à literatura britânica não era uma das principais prioridades da esfera cultural húngara, parece evidente que a literatura canónica britânica teve uma recepção mais favorável na Hungria comunista do que no Estado Novo.

Mining biomedical information from scientific literature

The rapid evolution and proliferation of a world-wide computerized network, the Internet, resulted in an overwhelming and constantly growing amount of publicly available data and information, a fact that was also verified in biomedicine. However, the lack of structure of textual data inhibits its direct processing by computational solutions. Information extraction is the task of text mining that intends to automatically collect information from unstructured text data sources. The goal of the work described in this thesis was to build innovative solutions for biomedical information extraction from scientific literature, through the development of simple software artifacts for developers and biocurators, delivering more accurate, usable and faster results. We started by tackling named entity recognition - a crucial initial task - with the development of Gimli, a machine-learning-based solution that follows an incremental approach to optimize extracted linguistic characteristics for each concept type. Afterwards, Totum was built to harmonize concept names provided by heterogeneous systems, delivering a robust solution with improved performance results. Such approach takes advantage of heterogenous corpora to deliver cross-corpus harmonization that is not constrained to specific characteristics. Since previous solutions do not provide links to knowledge bases, Neji was built to streamline the development of complex and custom solutions for biomedical concept name recognition and normalization. This was achieved through a modular and flexible framework focused on speed and performance, integrating a large amount of processing modules optimized for the biomedical domain. To offer on-demand heterogenous biomedical concept identification, we developed BeCAS, a web application, service and widget. We also tackled relation mining by developing TrigNER, a machine-learning-based solution for biomedical event trigger recognition, which applies an automatic algorithm to obtain the best linguistic features and model parameters for each event type. Finally, in order to assist biocurators, Egas was developed to support rapid, interactive and real-time collaborative curation of biomedical documents, through manual and automatic in-line annotation of concepts and relations. Overall, the research work presented in this thesis contributed to a more accurate update of current biomedical knowledge bases, towards improved hypothesis generation and knowledge discovery.