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The process of protecting sensitive data is continually growing and becoming increasingly important, especially as a result of the directives and laws imposed by the European Union. The effort to create automatic systems is continuous, but, in most cases, the processes behind them are still manual or semi-automatic. In this work, we have developed a component that can extract and classify sensitive data, from unstructured text information in European Portuguese. The objective was to create a system that allows organizations to understand their data and comply with legal and security purposes. We studied a hybrid approach to the problem of Named Entity Recognition for the Portuguese language. This approach combines several techniques such as rule-based/lexical-based models, machine learning algorithms, and neural networks. The rule-based and lexical-based approaches were used only for a set of specific classes. For the remaining classes of entities, two statistical models were tested&mdash, Conditional Random Fields and Random Forest and, finally, a Bidirectional-LSTM approach as experimented. Regarding the statistical models, we realized that Conditional Random Fields is the one that can obtain the best results, with a f1-score of 65.50%. With the Bi-LSTM approach, we have achieved a result of 83.01%. The corpora used for training and testing were HAREM Golden Collection, SIGARRA News Corpus, and DataSense NER Corpus.

A rich heritage of traditional pear varieties is kept in national Portuguese collections. Out of these varieties, &ldquo, Rocha&rdquo, dominates national pear production. Although a noticeable phenotypic variation among clones of this variety has been reported, little is known about its genetic variability, as to date molecular studies have been performed on a single &ldquo, clone. Eleven Simple Sequence Repeats (SSR) markers were used to assess the genetic diversity of 130 local cultivars, 80 of them being &ldquo, clones. The results allowed the differentiation of 75 genotypes of which 29 are &ldquo, Three synonyms groups and four homonymous groups of other local varieties were confirmed. A Bayesian model-based clustering approach identified two distinct clusters. Using flow cytometry, six cultivars were found to be triploids. These results show high genetic variability among &ldquo, clones. In conclusion, there is a need for different &ldquo, clones to be preserved to enable the correct selection of the multiplication material.