In this paper, we propose a hybrid machine learning approach to Information Extraction by combining conventional text classification techniques and Hidden Markov Models (HMM). A text classifier generates a (locally optimal) initial output, which is refined by an HMM, providing a globally optimal classification. The proposed approach was evaluated in two case studies and the experiments revealed a consistent gain in performance through the use of the HMM. In the first case study, the implemented prototype was used to extract information from bibliographic references, reaching a precision rate of 87.48% in a test set with 3000 references. In the second case study, the prototype extracted information from author affiliations, reaching a precision rate of 90.27% in a test set with 300 affiliations.
['Flávia A. Barros', 'Eduardo F. A. Silva', 'Ricardo Bastos Cavalcante Prudêncio', 'Valmir M. Filho', 'André C. A. Nascimento']
COMBINING TEXT CLASSIFIERS AND HIDDEN MARKOV MODELS FOR INFORMATION EXTRACTION