Performance analysis of a POS tagger applied to discharge summaries in Portuguese.
Part of speech taggers need a considerable amount of data to train their models. Such data is not readily available for medical texts in Portuguese. We evaluated the accuracy of a morphological tagger against a gold standard when trained with corpora of different sizes and domains. Accuracy was the highest with a medical corpus during the complete training process, achieving 91.5%. Training on a newswire corpus achieved 75.3% only. Furthermore, an active learning technique has been adapted to the POS tagging task. The algorithm uses a POS tagger committee to isolate the sentences with the highest disagreement indexes for manual correction. However, the method was not able to reduce training and tagging times when compared to a random selection strategy. We encourage that future works employ some effort in order to annotate a small amount of random data in the domain of study, which should be enough for higher accuracy rates.
Oleynik
Michel
M
Exact Sciences Technology Center, Pontifical Catholic University of Paraná, Curitiba, Brazil. michelole+medinfo2010@gmail.com
Nohama
Percy
P
Cancian
Pindaro Secco
PS
Schulz
Stefan
S
D000465:Algorithms; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D011174:Portugal; D012660:Semantics; D009626:Terminology as Topic