Segmentação de nome e endereço por meio de modelos escondidos de Markov e sua aplicação em processos de vinculação de registros

Abstract

The segmentation of names into its constituent parts is a fundamental step in the integration of databases by means of record linkage techniques. This segmentation can be accomplished in different ways. This study aimed to evaluate the use of Hidden Markov Models (HMM) in the segmentation names and addresses of people and the efficiency of the segmentation on the record linkage process. Databases of the Information System on Mortality (SIM in portuguese) and Information Subsystem for High Complexity Procedures (APAC in portuguese) of the state of Rio de Janeiro between 1999 and 2004 were used. A method composed of eight stages has been proposed for segmenting the names and addresses using routines implemented in PL/SQL and a library called JAHMM, a Java implementation of HMM algorithms. A random sample of 100 records in each database was used to verify the correctness of the segmentation process using the hidden Markov model. In order to verify the effect of segmenting the names through the HMM, three record linkage process were applied on a sample of the aforementioned databases, each of them using a different segmentation strategy, namely: 1) dividing the name into first name , last name, and middle initials; 2) division of the name into five parts; 3) segmentation by HMM. The HMM segmentation mechanism was in good agreement when compared to a human observer. The three linkage processes produced very similar results, with the first strategy performing a little better than the others. This study suggests that the segmentation of Brazilian names by means of HMM is not more efficient than the traditional segmentation methods.