{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T17:11:20Z","timestamp":1676394680480},"reference-count":18,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2009,4]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s0218213009000147","type":"journal-article","created":{"date-parts":[[2009,4,27]],"date-time":"2009-04-27T13:26:44Z","timestamp":1240838804000},"page":"311-329","source":"Crossref","is-referenced-by-count":3,"title":["COMBINING TEXT CLASSIFIERS AND HIDDEN MARKOV MODELS FOR INFORMATION EXTRACTION"],"prefix":"10.1142","volume":"18","author":[{"given":"FL\u00c1VIA A.","family":"BARROS","sequence":"first","affiliation":[{"name":"Center of Informatics, Federal University of Pernambuco, Pobox 7851 \u2013 CEP 50732-970 \u2013 Recife (PE), Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"EDUARDO F. 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