# Named Entity Recognition for Sensitive Data Discovery in Portuguese
## 3 NER Component
### Named Entity Recognition Module
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Entities Classes** & **Sensitive Data Included** \\ \hline \multirow{8}{*}{Personal Identification Number} & NumIdentifificacaoCivil & Identification Number \\  & IdentifificacaoBancaria & Bank Identification Number \\  & NumCartaoDeCredito & Credit Card Number \\  & NumIdentifificacaoFiscal & Tax Identification Number \\  & NumPassaport & Passport Number \\  & NumSegSocial & Social Security Number \\  & NumUtenteDeSaude & National Health Number \\  & ContactoTelefonico & Telephone Number \\  & NumCartaConducao & Driving License Number \\ \hline \multirow{8}{*}{Socio-Economic Information} & Pessoa & Person Names \\  & Local & Addresses, Locals \\ \cline{1-1}  & Organizacao & Organizations \\ \cline{1-1}  & Tempo & Dates \\ \cline{1-1}  & Valor & Values, Ordered values \\ \cline{1-1}  & Med & Medical data \\ \cline{1-1}  & Profissoa & Jobs, Professions \\ \hline Other & CodigoPostal & Postal Code \\ \cline{1-1}  & EnderecoEletronico & E-mail address \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classes of entities considered in this work. same entity. The first entity class is PESSOA, which corresponds to the names of people. For this implementation two different lexicons were used in order to catch both female and male names, and these lexicons can be obtained from the Public Administration Data Portal ([[https://dados.gov.pt/pt/datasets/nomesfeminino](https://dados.gov.pt/pt/datasets/nomesfeminino)]([https://dados.gov.pt/pt/datasets/nomesfeminino](https://dados.gov.pt/pt/datasets/nomesfeminino))). The LOCAL entity follows the same implementation used for the names above, as two lexicons were also used in this case, and each entry may correspond to more than one word, as is the case of 'United Kingdom'. The first lexicon, with more than 18,000 entries corresponds to the set of all Portuguese cities, municipalities, and parishes, available in Gov Data ([[https://dados.gov.pt/pt/datasets](https://dados.gov.pt/pt/datasets)]([https://dados.gov.pt/pt/datasets](https://dados.gov.pt/pt/datasets))). For the entities PROFISSAO and MED we have also used the comparison with lexicons, but using a different approach. The two lexicons are from Wikipedia ([[https://pt.wikipedia.org](https://pt.wikipedia.org)]([https://pt.wikipedia.org](https://pt.wikipedia.org))) information. The entity VALOR should extract all existing values from the text, which may correspond to the value of a contract, a fine, etc. In this type of entity, the value can be written both numerically and in full, and to cover both cases, we used Part-Of-Speech (POS) Tagging classification. For the words or symbols that come associated with the values, besides the use of the tag 'SYM' of POS Tagging, a lexicon has been created with the most relevant words that should be considered. This lexicon consists of words such as 'dollar', 'euro','millions', etc. The last entity implemented was TEMPO, and to be able to deal with it, a lexicon was created with all the months in Portuguese and English, as well as their abbreviated forms. Some of these entities were also implemented with Machine Learning models, with the goal of understanding how to achieve the best results for each class of sensitive data. 3. **Machine Learning Models:** Is the last subcomponent on the chain of the NER module. We conclude from the current state-of-the-art analysis that for the most ambiguous entities and for those in which there are no well-defined rules, the best results are achieved through machine learning methods and, out of these, the most recent approaches are based on the study of neural networks. In the next section, we present the Machine Learning approaches for NER used in this work.