NovaIntell - projecto de text-Mining para a língua portuguesa numa empresa de Gestão de Informação e Conhecimento

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Tradução comentada do livro de Abel Salazar "A Crise da Europa"

Este trabalho de projeto do Mestrado em Tradução e Interpretação Especializadas consiste na tradução, para inglês, do livro A Crise da Europa, de Abel Salazar. O protocolo celebrado entre a Casa-Museu Abel Salazar e o ISCAP deu o mote a esta colaboração que tem como objectivo final a edição da obra traduzida, possibilitando que o legado de Abel Salazar esteja cada vez mais acessível a um número crescente de pessoas. Este trabalho pretende, não só, tornar disponível a obra do autor a novos públicos através da sua publicação na que é hoje a língua universal – o inglês –, mas também dar conta de quem foi Abel Salazar em toda a sua soberba pluralidade. Para além da tradução, o presente trabalho leva a cabo uma análise da metodologia utilizada no processo tradutivo e das opções que foram tomadas na produção do texto de chegada. Finalmente, o desfasamento temporal entre o autor/texto fonte e o tradutor/texto de chegada é ilustrado, com recurso a exemplos que o demonstram e clarificam a postura metodológica da tradutora. A tradução desta obra é a primeira alguma vez feita de um livro de Abel Salazar para inglês e considero que será a primeira de muitas que poderão surgir da parceria com a Casa-Museu Abel Salazar.

Automatic syllabification for danish text-to-speech systems

In this paper, a rule-based automatic syllabifier for Danish is described using the Maximal Onset Principle. Prior success rates of rule-based methods applied to Portuguese and Catalan syllabification modules were on the basis of this work. The system was implemented and tested using a very small set of rules. The results gave rise to 96.9% and 98.7% of word accuracy rate, contrary to our initial expectations, being Danish a language with a complex syllabic structure and thus difficult to be rule-driven. Comparison with data-driven syllabification system using artificial neural networks showed a higher accuracy rate of the former system.

Text Mining: análise de sentimentos na classificação de notícias

Trabalho de Projeto apresentado como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

HIV-related discrimination in Sub-Sahran Africa: Explanatory characteristics and consequences on employment and HIV-testing

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Automatic information retrieval through text-mining

The dissertation presented for obtaining the Master’s Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

From Consciousness to Knowledge – The Explanatory Power of Revelation

Epistemology in philosophy of mind is a difficult endeavor. Those who believe that our phenomenal life is different from other domains suggest that self-knowledge about phenomenal properties is certain and therefore privileged. Usually, this so called privileged access is explained by the idea that we have direct access to our phenomenal life. This means, in contrast to perceptual knowledge, self-knowledge is non-inferential. It is widely believed that, this kind of directness involves two different senses: an epistemic sense and a metaphysical sense. Proponents of this view often claim that this is due to the fact that we are acquainted with our current experiences. The acquaintance thesis, therefore, is the backbone in justifying privileged access. Unfortunately the whole approach has a profound flaw. For the thesis to work, acquaintance has to be a genuine explanation. Since it is usually assumed that any knowledge relation between judgments and the corresponding objects are merely causal and contingent (e.g. in perception), the proponent of the privileged access view needs to show that acquaintance can do the job. In this thesis, however, I claim that the latter cannot be done. Based on considerations introduced by Levine, I conclude that this approach involves either the introduction of ontologically independent properties or a rather obscure knowledge relation. A proper explanation, however, cannot employ either of the two options. The acquaintance thesis is, therefore, bound to fail. Since the privileged access intuition seems to be vital to epistemology within the philosophy of mind, I will explore alternative justifications. After discussing a number of options, I will focus on the so called revelation thesis. This approach states that by simply having an experience with phenomenal properties, one is in the position to know the essence of those phenomenal properties. I will argue that, after finding a solution for the controversial essence claim, this thesis is a successful replacement explanation which maintains all the virtues of the acquaintance account without necessarily introducing ontologically independent properties or an obscure knowledge relation. The overall solution consists in qualifying the essence claim in the relevant sense, leaving us with an appropriate ontology for phenomenal properties. On the one hand, this avoids employing mysterious independent properties, since this ontological view is physicalist in nature. On the other hand, this approach has the right kind of structure to explain privileged self-knowledge of our phenomenal life. My final conclusion consists in the claim that the privileged access intuition is in fact veridical. It cannot, however, be justified by the popular acquaintance approach, but rather, is explainable by the controversial revelation thesis.

Hepatitis B and C in a Brazilian deaf community

ABSTRACTINTRODUCTION:Although deaf people are exposed to hepatitis B and C risk factors, epidemiological studies regarding these diseases in deaf people are lacking.METHODS:After watching an explanatory digital versatile disc (DVD) in Brazilian Sign Language, 88 deaf people were interviewed and tested for hepatitis B surface antigen (HBsAg), hepatitis B surface antibody (anti-HBs), hepatitis B core antibody (anti-HBc), and hepatitis C virus antibody (anti-HCV).RESULTS:The prevalence of hepatitis B markers was 8%; they were associated with incarceration and being born outside the State of São Paulo. No cases of hepatitis C were identified.CONCLUSIONS:Participants showed a substantial lack of knowledge regarding viral hepatitis, indicating a need for public policies that consider linguistic and cultural profiles.

Text mining e twitter : o poder das redes sociais num mercado competitivo

Actualmente, com a massificação da utilização das redes sociais, as empresas passam a sua mensagem nos seus canais de comunicação, mas os consumidores dão a sua opinião sobre ela. Argumentam, opinam, criticam (Nardi, Schiano, Gumbrecht, & Swartz, 2004). Positiva ou negativamente. Neste contexto o Text Mining surge como uma abordagem interessante para a resposta à necessidade de obter conhecimento a partir dos dados existentes. Neste trabalho utilizámos um algoritmo de Clustering hierárquico com o objectivo de descobrir temas distintos num conjunto de tweets obtidos ao longo de um determinado período de tempo para as empresas Burger King e McDonald’s. Com o intuito de compreender o sentimento associado a estes temas foi feita uma análise de sentimentos a cada tema encontrado, utilizando um algoritmo Bag-of-Words. Concluiu-se que o algoritmo de Clustering foi capaz de encontrar temas através do tweets obtidos, essencialmente ligados a produtos e serviços comercializados pelas empresas. O algoritmo de Sentiment Analysis atribuiu um sentimento a esses temas, permitindo compreender de entre os produtos/serviços identificados quais os que obtiveram uma polaridade positiva ou negativa, e deste modo sinalizar potencias situações problemáticas na estratégia das empresas, e situações positivas passíveis de identificação de decisões operacionais bem-sucedidas.

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

Reconstructing transcriptional regulatory networks using data integration and text mining

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature.

Text mining na análise de sentimentos em contextos de big data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação